BUG: KeyError after groupby().size() when index is multiindex and involves nan
- [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.

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

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

#### Code Sample, a copy-pastable example

```python
# Your code here
import pandas as pd

data = [[pd.NA, 10], [pd.NA,10], [2, pd.NA]]
df = pd.DataFrame(data, columns=['A1', 'A2'])
print(df)

counts = df.groupby(['A1', 'A2'],dropna=False).size()
print(counts)

# If you uncomment the lines below, it works
# pk_file = 'tmp.df'
# counts.to_pickle(pk_file)
# counts = pd.read_pickle(pk_file)

idx = counts.index[0]
print(counts.loc[idx])  # KeyError: (2.0, nan)
```

#### Problem description
Unable to access the Series returned from groupby using apparently valid key
Line "print(counts.loc[idx]" raises KeyError.
If I uncomment the lines that store/load the data with pickle, then it works. No KeyError is raised.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]

#### Expected Output
The Series returned from groupby is accessible with loc with valid keys
#### Output of ``pd.show_versions()``
<details>

[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
------------------
commit           : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python           : 3.8.5.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
Version          : Darwin Kernel Version 18.7.0: Thu Jun 18 20:50:10 PDT 2020; root:xnu-4903.278.43~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.1.2
numpy            : 1.19.1
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.1.1
setuptools       : 47.1.0
Cython           : 0.29.21
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       : 3.3.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : 1.5.2
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None
</details>
