Regression bugs when applying GroupBy Aggregations to Categorical columns

```python
>>> import pandas as pd

>>> ids = range(5)
>>> groups = pd.Series([0, 1, 1, 2, 2])
>>> values = pd.Categorical([0, 0, 0, 0, 1])
>>> df = pd.DataFrame({
            'id': ids,
            'groups': groups,
            'value': values
    }).set_index('id')

>>> df.groupby('groups').agg(pd.Series.nunique)
       value
groups
0          1
1          1
2        NaN

>>> df.groupby('groups').agg(pd.Series.nunique)['value'].dtype
CategoricalDtype(categories=[0, 1], ordered=False)

>>> df.groupby('groups').agg(pd.Series.count)
       value
groups
0          1
1        NaN
2        NaN

>>> df.groupby('groups').agg(pd.Series.mode)
                                                                           value
groups
0                                                                              0
1              0    0
Name: value, dtype: category
Categories (2, int64): [0, 1]
2       0    0
1    1
Name: value, dtype: category
Categories (2, int64): [0, 1]

```
#### Problem description
There's some regression bugs in `1.0.0rc0` when applying groupby aggregations to Categorical columns. All of these examples work as expected in `0.25.3`. 

Applying `pd.Series.nunique` and `pd.Series.count` both result in `NaN` for some groupings, the result has a Categorical dtype. The resulting dtype should be int64 and `NaN` should not be produced.

Applying `pd.Series.mode` returns the correct result for the first grouping but produces nonsensical output for subsequent groupings. 


#### Expected Output
Aggregations should work as expected on Categorical columns as they do in `0.25.3`:
```python
>>> df.groupby('groups').agg(pd.Series.nunique)
        value
groups
0           1
1           1
2           2

>>> df.groupby('groups').agg(pd.Series.nunique)['value'].dtype
dtype('int64')

>>> df.groupby('groups').agg(pd.Series.count)
        value
groups
0           1
1           2
2           2

>>> df.groupby('groups').agg(pd.Series.mode)
         value
groups
0      0     0
1      0     0
2      0     0
       1     1
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.9.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.0rc0
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.1
setuptools       : 45.1.0
Cython           : None
pytest           : 5.2.0
hypothesis       : None
sphinx           : 2.0.1
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.2.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : 0.3.2
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.0.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.2.0
s3fs             : 0.4.0
scipy            : 1.4.1
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : 0.47.0

</details>
