Categorical type doesn't survive a round-trip through parquet
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
import pyarrow as pa


df = pd.DataFrame({'x': pd.Categorical(['a', 'a', 'b', 'b'])})
df.dtypes  # category

# This works:
pa.Table.from_pandas(df).to_pandas().dtypes  # category

df.to_parquet("categories.parquet")
# This reads back object, but I expected category
pd.read_parquet("categories.parquet").dtypes  # object


# Numeric categories have the same issue:
df_num = pd.DataFrame({'x': pd.Categorical([1, 1, 2, 2])})
df_num.dtypes # category

pa.Table.from_pandas(df_num).to_pandas().dtypes  # category

df_num.to_parquet("categories_num.parquet")
# This reads back int64, but I expected category
pd.read_parquet("categories_num.parquet").dtypes  # int64

```
#### Problem description

Writing a string categorical variable to parquet is read back as string (`object` dtype). I expected it to be read as `category`.
The same thing happens if the category is numeric -- a numeric category is read back as int64. 

In the code above, I tried out an in-memory arrow table, which successfully translates categories back to pandas. Please let me know if you think this is an issue with the arrow project rather than pandas.

In the scheme of things, this isn't a big deal, but it's a small surprise.

#### Expected Output

I expected the data read by `pd.read_parquet` to be exactly the same as the data written by `df.to_parquet`. The values are the same, but the dtypes are not.


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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 5.0.0-15-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.24.2
pytest: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.8
numpy: 1.16.4
scipy: 1.2.1
pyarrow: 0.11.1
xarray: None
IPython: 7.5.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: 2.4.10
xlrd: 1.2.0
xlwt: None
xlsxwriter: 1.1.8
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.3
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: 0.3.0
pandas_gbq: None
pandas_datareader: None
gcsfs: None

</details>
