Pandas Corrupting PyArrow Integer Object Nulls
#### Code Sample, a copy-pastable example if possible

```python
pq_table = self.pq.read_table('file.parquet')
pandas = pq_table.to_pandas(integer_object_nulls=True)

modified_df = pandas.replace({ 'data': 'bad_data' }, 'good_data') # borked

self.pa.Table.from_pandas(modified_df, schema=pq_table.schema)
```
#### Problem description

When loading a parquet through `pyarrow` and converting to `pandas`, there is an option to use integer object nulls. This works as long as no action is performed on the data via pandas.

In this example, a parquet is loaded with a nullable int column. A DataFrame is resolved from this parquet with the `integer_object_nulls` flag set. For all intents and purposes, the DataFrame now _supports_ nullable ints. Pandas is used to replace `bad_data` with `good_data` in the `data` column, and in doing so, _upgrades_ the int to a float and replaces object nulls with NaN.

This shows pandas has the ability to hold onto nullable ints, but any subsequent operation on that dataframe corrupts the data. Pandas should respect the `integer_object_nulls` flag.

I'm not hopeful there is an official solution (after days of searching i've only found explanations why it works this way but no suggestions on how to move forward other than corrupting your own data), but if there there is a hidden feature, a workaround, or a hack, please let me know. We don't have the option _not_ to use nullable ints, so if this cannot be achieved in pandas I'll need to pivot to a different library.

I appreciate all of the work done and Pandas and apologize up front if I've come across as negative. I'm a bit frustrated.

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.4
pytest: None
pip: 18.1
setuptools: 39.1.0
Cython: None
numpy: 1.15.2
scipy: 1.1.0
pyarrow: 0.11.1
xarray: None
IPython: 6.4.0
sphinx: None
patsy: None
dateutil: 2.7.0
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: 2.5.3
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: 0.1.5
pandas_gbq: None
pandas_datareader: None

</details>
