groupby AssertionError with datetime column name
- [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.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

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

```python
# Your code here
import pandas as pd
import datetime

column_names = ['dimension_1', 'dt', 'value']
data = [
        ('D1', datetime.date(2020, 1, 1), 2.1),
        ('D1', datetime.date(2020, 1, 2), 4.1),
        ('D1', datetime.date(2020, 1, 3), 1.7),
    ]
df_stack = pd.DataFrame(data=data, columns=column_names)
df_stack['dt'] = pd.to_datetime(df_stack['dt'])
df_stack.set_index(['dimension_1', 'dt'], inplace=True)

df_pivot = df_stack.unstack(level=['dt']).transpose().droplevel(None).transpose()

agg_map = {c: 'sum' for c in df_pivot.columns}
df_pivot.groupby(level=['dimension_1']).agg(agg_map)
```

#### Problem description

When executing groupby (on index) for dataframe whose column-names are of type datetime ('datetime64[ns]'), the groupby fails with AssertionError (result.name == res_name). 

<details>
AssertionError                            Traceback (most recent call last)
<ipython-input-3-2655c5698d24> in <module>
      1 # do groupby on index
      2 agg_map = {c: 'sum' for c in df_pivot.columns}
----> 3 df_pivot.groupby(level=['dimension_1']).agg(agg_map)

c:\users\hli\appdata\local\programs\python\python38-32\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
    947             )
    948 
--> 949         result, how = self._aggregate(func, *args, **kwargs)
    950         if how is None:
    951             return result

c:\users\hli\appdata\local\programs\python\python38-32\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
    349                 keys = list(arg.keys())
    350                 if isinstance(obj, ABCDataFrame) and len(
--> 351                     obj.columns.intersection(keys)
    352                 ) != len(keys):
    353                     cols = sorted(set(keys) - set(obj.columns.intersection(keys)))

c:\users\hli\appdata\local\programs\python\python38-32\lib\site-packages\pandas\core\indexes\datetimelike.py in intersection(self, other, sort)
    701                     # TODO: no tests rely on this; needed?
    702                     result = result._with_freq("infer")
--> 703             assert result.name == res_name
    704             return result
    705 

</details>

#### Expected Output

The expected result in this example should be the dataframe itself, since it only has one row. This can be seen by changing the column data type to string:
```python
df_pivot.columns = df_pivot.columns.astype(str)
agg_map = {c: 'sum' for c in df_pivot.columns}
df_pivot.groupby(level=['dimension_1']).agg(agg_map)
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python           : 3.8.5.final.0
python-bits      : 32
OS               : Windows
OS-release       : 10
Version          : 10.0.18362
machine          : AMD64
processor        : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : English_United States.1252

pandas           : 1.1.1
numpy            : 1.19.0
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.2.1
setuptools       : 47.1.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.5.1
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.17.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.2.2
numexpr          : None
odfpy            : None
openpyxl         : 3.0.3
pandas_gbq       : None
pyarrow          : None
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : 1.5.0
sqlalchemy       : 1.3.18
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
