BUG: groupby(..., dropna=False).filter() never includes rows with NaNs in the index
### 

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

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


### Reproducible Example

```python
import pandas as pd

nan = float('nan')
df = pd.DataFrame([
    [1, 2, 1],
    [1, 2, 2],
    [2, nan, 3],
    [2, nan, 4],
    [3, 3, 5],],
                  columns=["a", "b", "c"])

gb = df.groupby(["a", "b"], dropna=False)
filtered = gb.filter(lambda x: len(x) > 1)
print(filtered)
#    a    b  c
# 0  1  2.0  1
# 1  1  2.0  2

# Expecting:
#    a    b  c
# 0  1  2.0  1
# 1  1  2.0  2
# 2  2  NaN  3
# 3  2  NaN  4


# Note that there is no problem with agg:
print()
agged = gb.agg(len)
print(agged)
#        c
# a b
# 1 2.0  2
# 2 NaN  2
# 3 3.0  1
```


### Issue Description

When using `groupby()` and then applying a filter, if the group values include a `NaN`, those rows are *always dropped*! If you add `dropna=False` to the `filter` call, it just fills them with NaNs.

### Expected Behavior

`NaN`s should be treated as unique values rather than dropped automatically.

### Installed Versions

<details>


INSTALLED VERSIONS
------------------
commit           : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python           : 3.9.4.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.14.16-arch1-1
machine          : x86_64
processor        : 
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.3.4
numpy            : 1.21.4
pytz             : 2021.3
dateutil         : 2.8.2
pip              : 21.3.1
setuptools       : 58.3.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
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          : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None
</details>
