BUG: Can't save pyarrow.lib.Decimal128Scalar to_parquet
- [ 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
Converting value to pyarrow.lib.Decimal128Scalar works in pyarrow: 
```python
>>> t = pyarrow.array([decimal.Decimal('2478463143.1140136719')], pyarrow.decimal128(38,10))[0]
>>> print(t)
2478463143.1140136719
>>>
```

However when attempting to write this value as one of the columns to parquet (**_to_parquet_**) the following happens:

> pyarrow.lib.ArrowInvalid: ('Could not convert 2478463143.1140136719 with type pyarrow.lib.Decimal128Scalar: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column X with type object')

#### Problem description

It should be possible to write this value to parquet since it is a valid decimal with precision and scale

#### Expected Output

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

<details>

INSTALLED VERSIONS
------------------
commit           : 9d598a5e1eee26df95b3910e3f2934890d062caa
python           : 3.8.6.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.18362
machine          : AMD64
processor        : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None

pandas           : 1.2.1
numpy            : 1.19.5
pytz             : 2020.4
dateutil         : 2.8.1
pip              : 20.2.1
setuptools       : 49.2.1
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.6.2
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 2.0.0
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
