Cannot replace all occurences of infs and nans to None with a single df.replace
#### Example code

```python
>>> df = pd.DataFrame(
...         {'foo':[
...             np.nan,
...             np.inf,
...             1.1,
...             2.2
...             ],
...         'bar':[
...             1,
...             -np.inf,
...             2.1,
...             3.2
...             ]
...         })
>>> df
   foo  bar
0  NaN  1.0
1  inf -inf
2  1.1  2.1
3  2.2  3.2
>>> df.replace({np.nan: None, -np.inf:None, np.inf:None})
   foo  bar
0  NaN  1.0
1  NaN  NaN
2  1.1  2.1
3  2.2  3.2
>>> 
```
#### Problem description
When trying to replace all occurrences of `NaN`s and `Inf`s to `None`s the resulting dataframe simply contains 'NaNs' for every occurrence, retaining the dtype of `float64` rather than the expected `object`.  A work around is to simply issue the replace again with only `np.nan` to `None`. 

This behavior was discovered when trying to convert the contents of a dataframe containing `Inf`s, and `NaN`s to `None`s in preparation for JSON compliant output via a dictionary for compatibility with third party tools rather than directly through panda's to_json() method (which admittedly would make this process unnecessary as it produces the compliant output already)

I strongly suspect this problem is related to inconsistencies outlined here: https://github.com/pandas-dev/pandas/issues/29024

#### Expected Output

I would expect a column type of `object` with `None`s in place as I see when replacing only NaNs.

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.8.1.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.4.13-arch1-1
machine          : x86_64
processor        : 
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.0rc0+233.gec0996c675
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.3
setuptools       : 44.0.0
Cython           : 0.29.14
pytest           : None
hypothesis       : None
sphinx           : 2.2.1
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.4.2
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.11.1
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.2
matplotlib       : 3.1.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.12
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
