DataFrame groupby with categoricals and aggreggation with pd.DataFrame.sum with skipna leads to wrong column name
#### Problem description
Consider the following data frame:

```
df = pd.DataFrame(data=(('Bob', 2),  ('Greg', None), ('Greg', 6)), columns=['Name', 'Items'])
```

```
   Name  Items
0   Bob    2.0
1  Greg    NaN
2  Greg    6.0
```

Now I want to group by `Name` and sum the `Items`, but I want the sum to be NaN if there are NaN elements. Due to a bug in pandas (https://github.com/pandas-dev/pandas/issues/20824) I cannot simply do

```
df.groupby('Name', observed=True).sum(skipna=False).reset_index()
```

because that results in:

```
   Name  Items
0   Bob    2.0
1  Greg    6.0
```

which is wrong because it's skipping the `NaN` for Greg even though it shouldn't (hence the bug). Thus I'm using the following workaround to get the correct result:

```
df.groupby('Name', observed=True).agg(pd.DataFrame.sum, skipna=False).reset_index()
```

which results in the expected:

```
   Name  Items
0   Bob    2.0
1  Greg    NaN
```

However, if we change the `Name` column to categorical then the resulting column names are wrong:

```
df_cat = df.copy()
df_cat['Name'] = df_cat['Name'].astype('category')
df_cat.groupby('Name', observed=True).agg(pd.DataFrame.sum, skipna=False).reset_index()
```

which prints:

```
  index  Items
0   Bob    2.0
1  Greg    NaN
```

As you can see, **the column that should be labelled `Name` is now called `index`.**

#### Expected Output
The same as the non-categorical version, i.e.:
```
   Name  Items
0   Bob    2.0
1  Greg    NaN
```

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

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Windows
OS-release       : 7
machine          : AMD64
processor        : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 0.25.1
numpy            : 1.16.3
pytz             : 2019.1
dateutil         : 2.8.0
pip              : 19.1
setuptools       : 41.0.1
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.2.5
html5lib         : None
pymysql          : None
psycopg2         : 2.8.2 (dt dec pq3 ext lo64)
jinja2           : 2.10.1
IPython          : 7.5.0
pandas_datareader: None
bs4              : 4.7.1
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.2.5
matplotlib       : 3.0.3
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.13.0
pytables         : None
s3fs             : None
scipy            : 1.2.1
sqlalchemy       : 1.3.3
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
