BUG: Inconsistent results of value_counts and mode for different nan-values
- [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.

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


```python
import pandas as pd
import numpy as np
s=pd.Series([True, pd.NA, np.nan])
s.value_counts(dropna=False)
print(s.value_counts(dropna=False))
print(s.mode(dropna=False))
```

#### Problem description

For `s.value_counts(...)` the result is

```
NaN     2
True    1
dtype: int64
```

but for `s.mode(...)` the output is:

```
0     NaN
1    True
2    <NA>
dtype: object
```

#### Expected Output

for the sake of consistancy one would expect `mode` to be only `NaN`, as it was counted twice with `value_counts`, i.e.

```
0     NaN
dtype: object
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : b5958ee1999e9aead1938c0bba2b674378807b3d
python           : 3.7.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.4.0-53-generic
Version          : #74-Ubuntu SMP Fri Dec 2 15:59:10 UTC 2016
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.1.5
numpy            : 1.19.2
pytz             : 2020.5
dateutil         : 2.8.1
pip              : 20.3.3
setuptools       : 51.0.0.post20201207
Cython           : 0.29.21
pytest           : 5.4.3
hypothesis       : 5.36.0
sphinx           : None
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              : 4.9.3
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : 1.2.1
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : 0.50.1

</details>
