Aggregation fails when grouping on more than one key and the column to aggregate is a tz-aware timestamp series
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
(
    pd.DataFrame(
        data=[[pd.Timestamp('2019-11-11 09:00:00+00:00'), 'x1', 'y1']],
        columns=['timestamp', 'x', 'y']
    )
    .groupby(['x', 'y'])
    .agg({'timestamp': lambda series: None})
)
```
#### Problem description
Potentially relates to #23683.

When upgrading from Pandas version 0.24.2 to 0.25.3 I noticed some of my old code failed of a sudden.

The above code isolates the issue: when grouping on more than one key and aggregating a Timestamp column, the `_try_cast()` method is called twice from inside `_python_agg_general()` method - the first call transforms the object type from Series to DatetimeArray and the second call fails because the new object doesn't have the `_values` attribute.

If the aggregated column is not of Timestamp dtype but eg., string, the code runs just fine:
```python
import pandas as pd
(
    pd.DataFrame(
        data=[['2019-11-11 09:00:00+00:00', 'x1', 'y1']],
        columns=['timestamp', 'x', 'y']
    )
    .groupby(['x', 'y'])
    .agg({'timestamp': lambda series: None})
)
```

If we keep the Timestamp dtype but remove the timezone, the code runs just fine:
```python
import pandas as pd
(
    pd.DataFrame(
        data=[[pd.Timestamp('2019-11-11 09:00:00'), 'x1', 'y1']],
        columns=['timestamp', 'x', 'y']
    )
    .groupby(['x', 'y'])
    .agg({'timestamp': lambda series: None})
)
```

If we keep the Timestamp dtype and the timezone but aggregate on a single key, the code runs just fine:
```python
import pandas as pd
(
    pd.DataFrame(
        data=[[pd.Timestamp('2019-11-11 09:00:00+00:00'), 'x1', 'y1']],
        columns=['timestamp', 'x', 'y']
    )
    .groupby(['x'])
    .agg({'timestamp': lambda series: None})
)
```

Running the initial code sample produces the following exception:
```python
AttributeError                            Traceback (most recent call last)
<ipython-input-130-fb69a372d092> in <module>()
      8     )
      9     .groupby(['x', 'y'])
---> 10     .agg({'timestamp': lambda series: None})
     11 )

~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/groupby/generic.py in aggregate(self, arg, *args, **kwargs)
   1453     @Appender(_shared_docs["aggregate"])
   1454     def aggregate(self, arg=None, *args, **kwargs):
-> 1455         return super().aggregate(arg, *args, **kwargs)
   1456 
   1457     agg = aggregate

~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/groupby/generic.py in aggregate(self, func, *args, **kwargs)
    227         func = _maybe_mangle_lambdas(func)
    228 
--> 229         result, how = self._aggregate(func, _level=_level, *args, **kwargs)
    230         if how is None:
    231             return result

~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
    504 
    505                 try:
--> 506                     result = _agg(arg, _agg_1dim)
    507                 except SpecificationError:
    508 

~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/base.py in _agg(arg, func)
    454                 result = OrderedDict()
    455                 for fname, agg_how in arg.items():
--> 456                     result[fname] = func(fname, agg_how)
    457                 return result
    458 

~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/base.py in _agg_1dim(name, how, subset)
    438                         "nested dictionary is ambiguous " "in aggregation"
    439                     )
--> 440                 return colg.aggregate(how, _level=(_level or 0) + 1)
    441 
    442             def _agg_2dim(name, how):

~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/groupby/generic.py in aggregate(self, func_or_funcs, *args, **kwargs)
    858 
    859             if self.grouper.nkeys > 1:
--> 860                 return self._python_agg_general(func_or_funcs, *args, **kwargs)
    861 
    862             try:

~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/groupby/groupby.py in _python_agg_general(self, func, *args, **kwargs)
    916                     values = ensure_float(values)
    917 
--> 918                 output[name] = self._try_cast(values[mask], result)
    919 
    920         return self._wrap_aggregated_output(output)

~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/groupby/groupby.py in _try_cast(self, result, obj, numeric_only)
    805                 # to the target timezone
    806                 try:
--> 807                     result = obj._values._from_sequence(
    808                         result, dtype="datetime64[ns, UTC]"
    809                     )

AttributeError: 'DatetimeArray' object has no attribute '_values'
```

#### Expected Output
<pre>
                timestamp
x	y	
x1	y1	NaT
</pre>
<details>

#### Output of ``pd.show_versions()``
$ python -c 'import pandas as pd; pd.show_versions()'

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.7.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : en_US.UTF-8
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.3
numpy            : 1.17.2
pytz             : 2018.5
dateutil         : 2.7.3
pip              : 18.0
setuptools       : 40.4.0
Cython           : None
pytest           : 5.3.0
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 1.1.2
lxml.etree       : 4.3.2
html5lib         : 1.0.1
pymysql          : 0.9.2
psycopg2         : None
jinja2           : 2.10
IPython          : 6.5.0
pandas_datareader: None
bs4              : 4.6.3
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.3.2
matplotlib       : 3.0.0
numexpr          : 2.7.0
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.14.1
pytables         : None
s3fs             : None
scipy            : 1.1.0
sqlalchemy       : 1.2.11
tables           : 3.5.2
xarray           : None
xlrd             : 1.1.0
xlwt             : 1.3.0
xlsxwriter       : 1.1.2
</details>
