BUG: RangeIndex is being materialized to a column while writing to parquet file when `index=True`
- [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.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

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

```python
In[20]: import pandas as pd
In[21]: import pyarrow as pa
In[22]: pdf = pd.DataFrame({'a':[1, 2, 3], 'b':[10, 11, 12]}, index=pd.RangeIndex(2, 5, 1))
In[23]: pdf.to_parquet('temp', index=True)
In[24]: pdf
Out[24]: 
   a   b
2  1  10
3  2  11
4  3  12
In[25]: pd.read_parquet('temp')
Out[25]: 
   a   b
2  1  10
3  2  11
4  3  12
In[26]: x = pa.parquet.read_metadata('temp')
In[27]: x.metadata
Out[27]: 
{b'ARROW:schema': b'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',
 b'pandas': b'{"index_columns": ["__index_level_0__"], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "a", "field_name": "a", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "b", "field_name": "b", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": null, "field_name": "__index_level_0__", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}], "creator": {"library": "pyarrow", "version": "1.0.1"}, "pandas_version": "1.1.4"}'}
In[28]: pd.read_parquet('temp').index
Out[28]: Int64Index([2, 3, 4], dtype='int64')
In[29]: pdf.index
Out[29]: RangeIndex(start=2, stop=5, step=1)
```

#### Problem description

The RangeIndex object should not be materialized and stored in the parquet file. Instead pandas should be storing the rangeIndex in metadata under `index_columns` like below when we pass `index=None`.


#### Expected Output
```python
In[32]: pdf.to_parquet('temp', index=None)
In[33]: pd.read_parquet('temp')
Out[33]: 
   a   b
2  1  10
3  2  11
4  3  12
In[34]: pd.read_parquet('temp').index
Out[34]: RangeIndex(start=2, stop=5, step=1)
In[35]: x = pa.parquet.read_metadata('temp')
In[36]: x.metadata
Out[36]: 
{b'ARROW:schema': b'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',
 b'pandas': b'{"index_columns": [{"kind": "range", "name": null, "start": 2, "stop": 5, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "a", "field_name": "a", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "b", "field_name": "b", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}], "creator": {"library": "pyarrow", "version": "1.0.1"}, "pandas_version": "1.1.4"}'}
```
#### Output of ``pd.show_versions()``

<details>

[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit           : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python           : 3.7.8.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.4.0-53-generic
Version          : #59-Ubuntu SMP Wed Oct 21 09:38:44 UTC 2020
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8
pandas           : 1.1.4
numpy            : 1.19.4
pytz             : 2020.4
dateutil         : 2.8.1
pip              : 20.2.4
setuptools       : 49.6.0.post20201009
Cython           : 0.29.21
pytest           : 6.1.2
hypothesis       : 5.41.1
sphinx           : 3.3.0
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.19.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : 0.8.4
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 1.0.1
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : 0.51.2
</details>
