BUG: None is replaced with Nan

[George3d6]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.
  Note: Env bellow is not the latest, but I also tested with 1.3.3 and behavior remains the same

• I have confirmed this bug exists on the master branch of pandas.

Reproducible Example

import pandas as pd
df = pd.DataFrame()
df['a'] = [1,2,3,4,5,6,7,8,None,None]
print(df['a'])

> Outputs:
0    1.0
1    2.0
2    3.0
3    4.0
4    5.0
5    6.0
6    7.0
7    8.0
8    NaN
9    NaN
Name: a, dtype: float64

Issue Description

Pandas is replacing None with NaN these are no the same, this is horrible and will cause bugs

Expected Behavior

I'd expected values I assign to an element or column in a pandas data frame to either:

1. Get assigned
2. Raise and error if they can't be assigned

Installed Versions

pd.show_versions()

INSTALLED VERSIONS

commit : 67a3d42
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-34-generic
Version : #36~20.04.1-Ubuntu SMP Fri Aug 27 08:06:32 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8

pandas : 1.1.4
numpy : 1.21.2
pytz : 2019.3
dateutil : 2.7.3
pip : 20.0.2
setuptools : 57.4.0
Cython : 0.29.21
pytest : 6.1.2
hypothesis : None
sphinx : 4.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : 1.4.23
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.53.1

[jreback]

https://pandas.pydata.org/docs/dev/user_guide/missing_data.html

None is an object snd cannot be represented as such in a numeric array

you can use object dtype and it will preserve with a performance penalty

[George3d6]

But converting to object doesn't actually work in most cases.

Pandas randomly decide to convert object typed column backed into ints and there's no reliable way to "force" it to use object.