BUG: `DataFrame.to_parquet` does not correctly write index information if `partition_cols` are provided
- [x] I have checked that this issue has not already been reported.

- [x] I have confirmed this bug exists on the latest version of pandas.

---

#### Code Sample, a copy-pastable example

```python
df = pd.DataFrame({'Data': [1, 2], 'partition': [1, 2]}, index=['2000-01-01', '2010-01-02'])

data_path_with_partitions = 'with_partitions.parquet'
df.to_parquet(data_path_with_partitions, partition_cols=['partition'])
df_read_with_partitions = pd.read_parquet(data_path_with_partitions)
pd.testing.assert_frame_equal(df, df_read_with_partitions)  # <-- this fails because the index has been turned into an extra column __index_level_0
```
results in an `AssertionError`:
```
>       pd.testing.assert_frame_equal(df, df_read_with_partitions)  # <-- this fails because the index has been turned into an extra column __index_level_0
E       AssertionError: DataFrame are different
E       
E       DataFrame shape mismatch
E       [left]:  (2, 2)
E       [right]: (2, 3)
```
Because of an extra column `__index_level_0__` containing the index values
```
   Data __index_level_0__ partition
0     1        2000-01-01         1
1     2        2010-01-02         2
```
On the other hand, without partitions works as expected:
```python
data_path_without_partitions = 'without_partitions.parquet'
df.to_parquet(data_path_without_partitions)
df_read_without_partitions = pd.read_parquet(data_path_without_partitions)
pd.testing.assert_frame_equal(df, df_read_without_partitions)  # <-- this passes
```

#### Problem description

If serializing data with pyarrow parquet using the `partition_cols` parameter, deserializing does not correctly reset the index, even though it has been serialized.

In the above example, `df_read_with_partitions` contains an extra column `__index_level_0__`.

Debugging into this, I believe this is a problem with the pandas integration inside pyarrow. In `pyarrow/parquet.py:1723` the sub-tables for each of the partitions get generated, but the `b'pandas'` metadata incorrectly overwrites the index column information of `subschema`.

#### Expected Output

I expect `read_parquet` to yield the same output whether or not it has been written with `partition_cols`.

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None
pandas           : 1.0.4
numpy            : 1.17.3
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.0.3
setuptools       : 40.8.0
Cython           : None
pytest           : 5.2.2
hypothesis       : None
sphinx           : 2.3.1
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.15.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : 0.3.2
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.17.1
pytables         : None
pytest           : 5.2.2
pyxlsb           : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.10
tables           : None
tabulate         : None
xarray           : 0.14.1
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : 0.46.0

</details>
