REGR: groupby().agg fails on categorical column in pandas 1.0.0
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
tbl = pd.DataFrame({"col_num": [1, 1, 2, 3]})
tbl["col_cat"] = tbl["col_num"].astype("category")

# The following line works in 0.25.3 but throws exception in 1.0.0
df = tbl.groupby("col_num").agg({"col_cat": "first"})
```
#### Problem description
agg() on categorical column works without any warnings in 0.25.3 but throws exception in pandas 1.0.0:
<details>
NotImplementedError                       Traceback (most recent call last)
<ipython-input-2-663038a86e20> in <module>
----> 1 df = tbl.groupby("col_num").agg({"col_cat": "first"})

~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, *args, **kwargs)
    938         func = _maybe_mangle_lambdas(func)
    939 
--> 940         result, how = self._aggregate(func, *args, **kwargs)
    941         if how is None:
    942             return result

~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
    426 
    427                 try:
--> 428                     result = _agg(arg, _agg_1dim)
    429                 except SpecificationError:
    430 

~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\base.py in _agg(arg, func)
    393                 result = {}
    394                 for fname, agg_how in arg.items():
--> 395                     result[fname] = func(fname, agg_how)
    396                 return result
    397 

~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\base.py in _agg_1dim(name, how, subset)
    377                         "nested dictionary is ambiguous in aggregation"
    378                     )
--> 379                 return colg.aggregate(how)
    380 
    381             def _agg_2dim(name, how):

~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, *args, **kwargs)
    245 
    246         if isinstance(func, str):
--> 247             return getattr(self, func)(*args, **kwargs)
    248 
    249         elif isinstance(func, abc.Iterable):

~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\groupby\groupby.py in f(self, **kwargs)
   1376                 # try a cython aggregation if we can
   1377                 try:
-> 1378                     return self._cython_agg_general(alias, alt=npfunc, **kwargs)
   1379                 except DataError:
   1380                     pass

~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\groupby\groupby.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
    888 
    889             result, agg_names = self.grouper.aggregate(
--> 890                 obj._values, how, min_count=min_count
    891             )
    892 

~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\groupby\ops.py in aggregate(self, values, how, axis, min_count)
    579     ) -> Tuple[np.ndarray, Optional[List[str]]]:
    580         return self._cython_operation(
--> 581             "aggregate", values, how, axis, min_count=min_count
    582         )
    583 

~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\groupby\ops.py in _cython_operation(self, kind, values, how, axis, min_count, **kwargs)
    453         # are not setup for dim transforming
    454         if is_categorical_dtype(values) or is_sparse(values):
--> 455             raise NotImplementedError(f"{values.dtype} dtype not supported")
    456         elif is_datetime64_any_dtype(values):
    457             if how in ["add", "prod", "cumsum", "cumprod"]:

NotImplementedError: category dtype not supported
</details>
#### Expected Output
no error, just groupped dataframe.

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.6.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 1.0.0
numpy            : 1.17.5
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 45.1.0.post20200119
Cython           : None
pytest           : 5.3.5
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 1.2.7
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.0
IPython          : 7.11.1
pandas_datareader: None
bs4              : 4.8.2
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.3.5
pyxlsb           : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : 1.2.7
numba            : None
</details>
