UnknownTimeZoneError: 'tzutc()' when writing a dataframe using from_dict() then reading it using read_parquet()
#### Code Sample

```python
import datetime
import fastparquet
import pandas as pd

from dateutil.tz import tzutc

def setup():
    # note: mock or real API call results in the same error
    data = mock_list_s3_bucket()
    df = pd.DataFrame.from_dict(data)
    #print(data)
    df.to_parquet(path, engine='auto', compression='snappy')

def main():
    # error happens here - pytz.exceptions.UnknownTimeZoneError: 'tzutc()'
    df = pd.read_parquet(path, engine='auto')
    print(df)

def mock_list_s3_bucket():
    # mock a call to S3 API
    return [
        {'Key': 'fun/file1.txt', 'LastModified': datetime.datetime(2018, 6, 7, 2, 59, 59, tzinfo=tzutc()), 'ETag': '"8c768c05b4faea563a8520acb983fb79"', 'Size': 4445, 'StorageClass': 'GLACIER'},
        {'Key': 'fun/file2.txt', 'LastModified': datetime.datetime(2018, 6, 7, 2, 59, 59, tzinfo=tzutc()), 'ETag': '"8c768c05b4faea563a8520acb983fb79"', 'Size': 4445, 'StorageClass': 'GLACIER'},
        {'Key': 'fun/file3.txt', 'LastModified': datetime.datetime(2018, 6, 7, 2, 59, 59, tzinfo=tzutc()), 'ETag': '"8c768c05b4faea563a8520acb983fb79"', 'Size': 4445, 'StorageClass': 'GLACIER'}
    ]

if __name__ == "__main__":
    setup()
    main()
```
#### Problem description

```
  File "pandas\_libs\tslibs\timezones.pyx", line 84, in pandas._libs.tslibs.timezones.maybe_get_tz
  File "pandas\_libs\tslibs\timezones.pyx", line 99, in pandas._libs.tslibs.timezones.maybe_get_tz
  File "C:\Users\userx\AppData\Local\Programs\Python\Python37\lib\site-packages\pytz\__init__.py", line 178, in timezone
    raise UnknownTimeZoneError(zone)
pytz.exceptions.UnknownTimeZoneError: 'tzutc()'
```

I have an app that reads bucket list data on a periodic basis from the S3 API. Up until recently, everything worked fine. When we upgraded to pandas 0.24 the problem with the parquet files being generated started to surface.

**Note**: I created a clean VM, installed pandas 0.24 and all dependencies, and was able to reproduce the issue.

Here is more info on the column metadata generated by fastparquet.

**pandas 0.23 metadata**
```
{"columns": [{"metadata": null, "name": "ETag", "numpy_type": "object", "pandas_type": "unicode"}, {"metadata": null, "name": "Key", "numpy_type": "object", "pandas_type": "unicode"}, {"metadata": {"timezone": "UTC"}, "name": "LastModified", "numpy_type": "datetime64[ns, UTC]", "pandas_type": "datetimetz"}, {"metadata": null, "name": "Size", "numpy_type": "int64", "pandas_type": "int64"}, {"metadata": null, "name": "StorageClass", "numpy_type": "object", "pandas_type": "unicode"}], "index_columns": [], "pandas_version": "0.23.4"}
```

**pandas 0.24 metadata**
```
{"columns": [{"metadata": null, "name": "ETag", "numpy_type": "object", "pandas_type": "unicode"}, {"metadata": null, "name": "Key", "numpy_type": "object", "pandas_type": "unicode"}, {"metadata": {"timezone": "tzutc()"}, "name": "LastModified", "numpy_type": "datetime64[ns, tzutc()]", "pandas_type": "datetimetz"}, {"metadata": null, "name": "Size", "numpy_type": "int64", "pandas_type": "int64"}, {"metadata": null, "name": "StorageClass", "numpy_type": "object", "pandas_type": "unicode"}], "index_columns": [], "pandas_version": "0.24.1"}
```


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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 39.0.1
Cython: None
numpy: 1.16.1
scipy: None
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.5
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
