Parquet file written with Dask does not load using Pandas
#### Code Sample, a copy-pastable example if possible

```python
import numpy as np
import pandas as pd
import dask.dataframe as dd
from dask.diagnostics import ProgressBar
from dask.distributed import Client

client = Client('192.168.1.33:8786', processes=True)

path = "D:\\Downloads\\Wikipedia\\Yago\\"
names = ['dummy1', 'class1', 'statement', 'class2', 'value']
dtypes = {'dummy1': 'category', 'class1': 'category', 'statement': 'category', 'class2': np.str, 'value': 'category'}
cols = ['class1', 'statement', 'class2', 'value']
df = dd.read_csv(path + "yagoGeonamesOnlyData.tsv", sep="\t", skiprows=1, header=None, names=names, dtype=dtypes, usecols=cols, blocksize=1e7)

df.to_parquet(path + "yagoGeonamesOnlyData", engine='pyarrow', compression='BROTLI', write_index=False)    # Can't specify npartitions.
```
#### Problem description
The above written file cannot be loaded using Pandas:

`df = pd.read_parquet(path + "yagoGeonamesOnlyData", engine='pyarrow')`

but does load with Dask:

`df = dd.read_parquet(path + "yagoGeonamesOnlyData", engine='pyarrow', index=False)`

The pandas version gives the following error:

```python
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
 in 
      1 #df = dd.read_parquet(path + "yagoGeonamesOnlyData", engine='pyarrow', index=False)
----> 2 df = pd.read_parquet(path + "yagoGeonamesOnlyData", engine='pyarrow')

~\AppData\Roaming\Python\Python37\site-packages\pandas\io\parquet.py in read_parquet(path, engine, columns, **kwargs)
    294 
    295     impl = get_engine(engine)
--> 296     return impl.read(path, columns=columns, **kwargs)

~\AppData\Roaming\Python\Python37\site-packages\pandas\io\parquet.py in read(self, path, columns, **kwargs)
    123         kwargs["use_pandas_metadata"] = True
    124         result = self.api.parquet.read_table(
--> 125             path, columns=columns, **kwargs
    126         ).to_pandas()
    127         if should_close:

E:\WPy-3710\python-3.7.1.amd64\lib\site-packages\pyarrow\parquet.py in read_table(source, columns, use_threads, metadata, use_pandas_metadata, memory_map, read_dictionary, filesystem, filters, buffer_size)
   1272                             read_dictionary=read_dictionary,
   1273                             buffer_size=buffer_size,
-> 1274                             filesystem=filesystem, filters=filters)
   1275     else:
   1276         pf = ParquetFile(source, metadata=metadata,

E:\WPy-3710\python-3.7.1.amd64\lib\site-packages\pyarrow\parquet.py in __init__(self, path_or_paths, filesystem, schema, metadata, split_row_groups, validate_schema, filters, metadata_nthreads, read_dictionary, memory_map, buffer_size)
   1058 
   1059         if validate_schema:
-> 1060             self.validate_schemas()
   1061 
   1062     def equals(self, other):

E:\WPy-3710\python-3.7.1.amd64\lib\site-packages\pyarrow\parquet.py in validate_schemas(self)
   1111                                  '{1!s}\n\nvs\n\n{2!s}'
   1112                                  .format(piece, file_schema,
-> 1113                                          dataset_schema))
   1114 
   1115     def read(self, columns=None, use_threads=True, use_pandas_metadata=False):

ValueError: Schema in D:\Downloads\Wikipedia\Yago\yagoGeonamesOnlyData\part.439.parquet was different. 
class1: dictionary
statement: dictionary
class2: string
value: null
metadata
--------
{b'ARROW:schema': b'/////zAEAAAUAAAAAAAAAAAACgAOAAYABQAIAAoAAAAAAQMAEAAAAAAA'
                  b'CgAMAAAABAAIAAoAAADUAgAABAAAAAEAAAAMAAAACAAMAAQACAAIAAAA'
                  b'CAAAABAAAAAGAAAAcGFuZGFzAACeAgAAeyJpbmRleF9jb2x1bW5zIjog'
                  b'W10sICJjb2x1bW5faW5kZXhlcyI6IFtdLCAiY29sdW1ucyI6IFt7Im5h'
                  b'bWUiOiAiY2xhc3MxIiwgImZpZWxkX25hbWUiOiAiY2xhc3MxIiwgInBh'
                  b'bmRhc190eXBlIjogImNhdGVnb3JpY2FsIiwgIm51bXB5X3R5cGUiOiAi'
                  b'aW50MzIiLCAibWV0YWRhdGEiOiB7Im51bV9jYXRlZ29yaWVzIjogMTEz'
                  b'MjY3LCAib3JkZXJlZCI6IGZhbHNlfX0sIHsibmFtZSI6ICJzdGF0ZW1l'
                  b'bnQiLCAiZmllbGRfbmFtZSI6ICJzdGF0ZW1lbnQiLCAicGFuZGFzX3R5'
                  b'cGUiOiAiY2F0ZWdvcmljYWwiLCAibnVtcHlfdHlwZSI6ICJpbnQ4Iiwg'
                  b'Im1ldGFkYXRhIjogeyJudW1fY2F0ZWdvcmllcyI6IDEsICJvcmRlcmVk'
                  b'IjogZmFsc2V9fSwgeyJuYW1lIjogImNsYXNzMiIsICJmaWVsZF9uYW1l'
                  b'IjogImNsYXNzMiIsICJwYW5kYXNfdHlwZSI6ICJ1bmljb2RlIiwgIm51'
                  b'bXB5X3R5cGUiOiAib2JqZWN0IiwgIm1ldGFkYXRhIjogbnVsbH0sIHsi'
                  b'bmFtZSI6ICJ2YWx1ZSIsICJmaWVsZF9uYW1lIjogInZhbHVlIiwgInBh'
                  b'bmRhc190eXBlIjogImVtcHR5IiwgIm51bXB5X3R5cGUiOiAib2JqZWN0'
                  b'IiwgIm1ldGFkYXRhIjogbnVsbH1dLCAiY3JlYXRvciI6IHsibGlicmFy'
                  b'eSI6ICJweWFycm93IiwgInZlcnNpb24iOiAiMC4xNS4xIn0sICJwYW5k'
                  b'YXNfdmVyc2lvbiI6ICIwLjI1LjMifQAABAAAANQAAABsAAAAQAAAAAQA'
                  b'AADY////AAABARQAAAAMAAAABAAAAAAAAAAU////BQAAAHZhbHVlAAAA'
                  b'EAAUAAgABgAHAAwAAAAQABAAAAAAAAEFFAAAAAwAAAAEAAAAAAAAAEz/'
                  b'//8GAAAAY2xhc3MyAACs////AAABBTwAAAA0AAAAEAAAACgAAAAIABAA'
                  b'CAAEAAgAAAAMAAAAAQAAAAAAAACk////AAAAAQgAAAAAAAAAnP///wkA'
                  b'AABzdGF0ZW1lbnQAAAAQABgACAAGAAcADAAQABQAEAAAAAAAAQVAAAAA'
                  b'OAAAABAAAAAoAAAACAAIAAAABAAIAAAADAAAAAgADAAIAAcACAAAAAAA'
                  b'AAEgAAAAAAAAAAQABAAEAAAABgAAAGNsYXNzMQAA',
 b'pandas': b'{"index_columns": [], "column_indexes": [], "columns": [{"name":'
            b' "class1", "field_name": "class1", "pandas_type": "categorical",'
            b' "numpy_type": "int32", "metadata": {"num_categories": 113267, "'
            b'ordered": false}}, {"name": "statement", "field_name": "statemen'
            b't", "pandas_type": "categorical", "numpy_type": "int8", "metadat'
            b'a": {"num_categories": 1, "ordered": false}}, {"name": "class2",'
            b' "field_name": "class2", "pandas_type": "unicode", "numpy_type":'
            b' "object", "metadata": null}, {"name": "value", "field_name": "v'
            b'alue", "pandas_type": "empty", "numpy_type": "object", "metadata'
            b'": null}], "creator": {"library": "pyarrow", "version": "0.15.1"'
            b'}, "pandas_version": "0.25.3"}'}

vs

class1: dictionary
statement: dictionary
class2: string
value: string
metadata
--------
{b'ARROW:schema': b'/////zAEAAAUAAAAAAAAAAAACgAOAAYABQAIAAoAAAAAAQMAEAAAAAAA'
                  b'CgAMAAAABAAIAAoAAADUAgAABAAAAAEAAAAMAAAACAAMAAQACAAIAAAA'
                  b'CAAAABAAAAAGAAAAcGFuZGFzAACfAgAAeyJpbmRleF9jb2x1bW5zIjog'
                  b'W10sICJjb2x1bW5faW5kZXhlcyI6IFtdLCAiY29sdW1ucyI6IFt7Im5h'
                  b'bWUiOiAiY2xhc3MxIiwgImZpZWxkX25hbWUiOiAiY2xhc3MxIiwgInBh'
                  b'bmRhc190eXBlIjogImNhdGVnb3JpY2FsIiwgIm51bXB5X3R5cGUiOiAi'
                  b'aW50MTYiLCAibWV0YWRhdGEiOiB7Im51bV9jYXRlZ29yaWVzIjogMTY1'
                  b'NzMsICJvcmRlcmVkIjogZmFsc2V9fSwgeyJuYW1lIjogInN0YXRlbWVu'
                  b'dCIsICJmaWVsZF9uYW1lIjogInN0YXRlbWVudCIsICJwYW5kYXNfdHlw'
                  b'ZSI6ICJjYXRlZ29yaWNhbCIsICJudW1weV90eXBlIjogImludDgiLCAi'
                  b'bWV0YWRhdGEiOiB7Im51bV9jYXRlZ29yaWVzIjogNCwgIm9yZGVyZWQi'
                  b'OiBmYWxzZX19LCB7Im5hbWUiOiAiY2xhc3MyIiwgImZpZWxkX25hbWUi'
                  b'OiAiY2xhc3MyIiwgInBhbmRhc190eXBlIjogInVuaWNvZGUiLCAibnVt'
                  b'cHlfdHlwZSI6ICJvYmplY3QiLCAibWV0YWRhdGEiOiBudWxsfSwgeyJu'
                  b'YW1lIjogInZhbHVlIiwgImZpZWxkX25hbWUiOiAidmFsdWUiLCAicGFu'
                  b'ZGFzX3R5cGUiOiAidW5pY29kZSIsICJudW1weV90eXBlIjogIm9iamVj'
                  b'dCIsICJtZXRhZGF0YSI6IG51bGx9XSwgImNyZWF0b3IiOiB7ImxpYnJh'
                  b'cnkiOiAicHlhcnJvdyIsICJ2ZXJzaW9uIjogIjAuMTUuMSJ9LCAicGFu'
                  b'ZGFzX3ZlcnNpb24iOiAiMC4yNS4zIn0ABAAAANQAAABsAAAAQAAAAAQA'
                  b'AADY////AAABBRQAAAAMAAAABAAAAAAAAAAU////BQAAAHZhbHVlAAAA'
                  b'EAAUAAgABgAHAAwAAAAQABAAAAAAAAEFFAAAAAwAAAAEAAAAAAAAAEz/'
                  b'//8GAAAAY2xhc3MyAACs////AAABBTwAAAA0AAAAEAAAACgAAAAIABAA'
                  b'CAAEAAgAAAAMAAAAAQAAAAAAAACk////AAAAAQgAAAAAAAAAnP///wkA'
                  b'AABzdGF0ZW1lbnQAAAAQABgACAAGAAcADAAQABQAEAAAAAAAAQVAAAAA'
                  b'OAAAABAAAAAoAAAACAAIAAAABAAIAAAADAAAAAgADAAIAAcACAAAAAAA'
                  b'AAEQAAAAAAAAAAQABAAEAAAABgAAAGNsYXNzMQAA',
 b'pandas': b'{"index_columns": [], "column_indexes": [], "columns": [{"name":'
            b' "class1", "field_name": "class1", "pandas_type": "categorical",'
            b' "numpy_type": "int16", "metadata": {"num_categories": 16573, "o'
            b'rdered": false}}, {"name": "statement", "field_name": "statement'
            b'", "pandas_type": "categorical", "numpy_type": "int8", "metadata'
            b'": {"num_categories": 4, "ordered": false}}, {"name": "class2", '
            b'"field_name": "class2", "pandas_type": "unicode", "numpy_type": '
            b'"object", "metadata": null}, {"name": "value", "field_name": "va'
            b'lue", "pandas_type": "unicode", "numpy_type": "object", "metadat'
            b'a": null}], "creator": {"library": "pyarrow", "version": "0.15.1'
            b'"}, "pandas_version": "0.25.3"}'}
```

#### Expected Output

For the file to be loaded without error.

#### Output of ``pd.show_versions()``

<details>

[paste the output of ``pd.show_versions()`` here below this line]
E:\WPy-3710\python-3.7.1.amd64\lib\site-packages\xarray\core\merge.py:10: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version
  PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.1.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 0.25.3
numpy            : 1.16.4
pytz             : 2018.6
dateutil         : 2.7.5
pip              : 19.3.1
setuptools       : 41.4.0
Cython           : 0.29.13
pytest           : 5.3.1
hypothesis       : 3.82.1
sphinx           : 2.2.1
blosc            : 1.6.1
feather          : 0.4.0
xlsxwriter       : 1.1.2
lxml.etree       : 4.3.4
html5lib         : 1.0.1
pymysql          : None
psycopg2         : 2.7.4 (dt dec pq3 ext lo64)
jinja2           : 2.10.1
IPython          : 7.6.1
pandas_datareader: None
bs4              : 4.6.3
bottleneck       : 1.2.1
fastparquet      : 0.3.2
gcsfs            : None
lxml.etree       : 4.3.4
matplotlib       : 3.0.3
numexpr          : 2.6.8
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.15.1
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.12
tables           : 3.4.4
xarray           : 0.11.3
xlrd             : 1.1.0
xlwt             : None
xlsxwriter       : 1.1.2
</details>
