to_parquet mishandles dates
#### Code Sample

This generates a series of ints for dates, which when trying to be imported elsewhere fails

```python
# Fails
dbSlice.to_parquet(outFile, index= False)
```

Instead, [consider something like this](https://medium.com/@andyreagan/tricks-for-coercing-pandas-into-parquet-d1137421db88) to run as a preprocessor for dates before saving as parquet as the default (or at least a flag):

```python
# Works
dbSlice['date'] = dbSlice.loc[~dbSlice.date.isnull(),'date'].apply(lambda x: x.isoformat())
dbSlice.to_parquet(outFile, index= False)
```

#### Problem description

The whole purpose of exporting the dataframe is data portability. Mangling data types is a big no-no, especially if there's no obvious way for other programs to read it. Exporting dates as an ISO string is easily interpretable by other software.

My particular use case wasn't even strange, just importing the parquet into Spark.

#### Expected Output

Any output interpretable by other software

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.4.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           : 0.25.2
numpy            : 1.17.3
pytz             : 2018.4
dateutil         : 2.7.2
pip              : 19.3
setuptools       : 41.0.1
Cython           : 0.27.3
pytest           : 4.3.1
hypothesis       : None
sphinx           : 1.6.6
blosc            : None
feather          : None
xlsxwriter       : 1.0.2
lxml.etree       : 4.1.1
html5lib         : 0.9999999
pymysql          : 0.8.0
psycopg2         : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2           : 2.10
IPython          : 6.2.1
pandas_datareader: None
bs4              : 4.6.0
bottleneck       : 1.2.1
fastparquet      : 0.3.1
gcsfs            : None
lxml.etree       : 4.1.1
matplotlib       : 2.2.3
numexpr          : 2.6.4
odfpy            : None
openpyxl         : 2.5.12
pandas_gbq       : None
pyarrow          : 0.14.0
pytables         : None
s3fs             : None
scipy            : 1.3.0
sqlalchemy       : 1.2.1
tables           : 3.4.2
xarray           : None
xlrd             : 1.1.0
xlwt             : 1.3.0
xlsxwriter       : 1.0.2

</details>
