BUG: df.groupby().count() returns NaN instead of Zero
- [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.

---

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

```python


import pandas as pd

df = pd.DataFrame(
    {
        "cat_1": pd.Categorical(list("AABB"), categories=list("ABC")),
        "cat_2": pd.Categorical(list("1111"), categories=list("12")),
        "value": [0.1, 0.1, 0.1, 0.1],
    }
)


# SeriesGroupBy on one pd.Categorical: unobserved categories have a count of 0
srg_grp = df.groupby(['cat_1'], observed=False)['value']
print(srg_grp.count())

# SeriesGroupBy on two pd.Categorical: unobserved categories have a count of 0
srs_grp = df.groupby(['cat_1', 'cat_2'], observed=False)['value']
print(srs_grp.count())

# DataFrameGroupBy on one pd.Categorical: unobserved categories have a count of 0
df_grp = df.groupby(['cat_1'], observed=False)
print(df_grp.count())

# DataFrameGroupBy on two pd.Categorical: unobserved categories have a count of NaN
df_grp = df.groupby(['cat_1', 'cat_2'], observed=False)
print(df_grp.count())
```

#### Problem description

When grouping by multiple pd.Categorical columns, DataFrameGroupBy.count() returns NaN for missing categories, and the dtype is float. 

A similar problem is reported for .sum() in #31422 

#### Expected Output

.count() should return zero for missing categories with a dtype of int. 

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

<details>

INSTALLED VERSIONS
------------------
commit           : 248c191478aabacffec47105c3fe6caf22b0dcc1
python           : 3.8.3.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.18362
machine          : AMD64
processor        : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : English_United Kingdom.1252

pandas           : 1.1.0.dev0+1973.g248c19147.dirty
numpy            : 1.18.5
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.1.1
setuptools       : 47.3.1.post20200616
Cython           : 0.29.20
pytest           : 5.4.3
hypothesis       : 5.18.0
sphinx           : 3.1.1
blosc            : None
feather          : None
xlsxwriter       : 1.2.9
lxml.etree       : 4.5.1
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.15.0
pandas_datareader: None
bs4              : 4.9.1
bottleneck       : 1.3.2
fsspec           : 0.7.4
fastparquet      : 0.4.0
gcsfs            : 0.6.2
matplotlib       : 3.2.1
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.3
pandas_gbq       : None
pyarrow          : 0.17.1
pytables         : None
pyxlsb           : None
s3fs             : 0.4.2
scipy            : 1.3.2
sqlalchemy       : 1.3.17
tables           : 3.6.1
tabulate         : 0.8.7
xarray           : 0.15.1
xlrd             : 1.2.0
xlwt             : 1.3.0
numba            : 0.48.0

</details>
