DOC: Remove mention that on with MultiIndex is supported with Rolling Groupby
- [.] I have checked that this issue has not already been reported.

- [.] I have confirmed this bug exists on the latest version 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

```

dates = pd.date_range(start='2016-01-01 09:30:00', periods=20, freq='s')
df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
                   'B': np.concatenate((dates, dates)),
                   'C': np.arange(40),})
df.set_index(['A', 'B'], inplace=True)
df.groupby('A').rolling('4s', on='B').C.mean()

```

#### Problem description

Multi-index dataframe gives the following error message:

> ValueError: invalid on specified as B, must be a column (of DataFrame), an Index or None

when one layer of the multi index is used for groupby, while the other is used as a rolling datetime level.

Similar to https://github.com/pandas-dev/pandas/issues/13966

#### Expected Output

```
A  B                  
1  2016-01-01 09:30:00     0.0
   2016-01-01 09:30:01     0.5
   2016-01-01 09:30:02     1.0
   2016-01-01 09:30:03     1.5
   2016-01-01 09:30:04     2.5
   2016-01-01 09:30:05     3.5
   2016-01-01 09:30:06     4.5
   2016-01-01 09:30:07     5.5
   2016-01-01 09:30:08     6.5
   2016-01-01 09:30:09     7.5
   2016-01-01 09:30:10     8.5
   2016-01-01 09:30:11     9.5
   2016-01-01 09:30:12    10.5
   2016-01-01 09:30:13    11.5
   2016-01-01 09:30:14    12.5
   2016-01-01 09:30:15    13.5
   2016-01-01 09:30:16    14.5
   2016-01-01 09:30:17    15.5
   2016-01-01 09:30:18    16.5
   2016-01-01 09:30:19    17.5
2  2016-01-01 09:30:00    20.0
   2016-01-01 09:30:01    20.5
   2016-01-01 09:30:02    21.0
   2016-01-01 09:30:03    21.5
   2016-01-01 09:30:04    22.5
   2016-01-01 09:30:05    23.5
   2016-01-01 09:30:06    24.5
   2016-01-01 09:30:07    25.5
   2016-01-01 09:30:08    26.5
   2016-01-01 09:30:09    27.5
   2016-01-01 09:30:10    28.5
   2016-01-01 09:30:11    29.5
3  2016-01-01 09:30:12    32.0
   2016-01-01 09:30:13    32.5
   2016-01-01 09:30:14    33.0
   2016-01-01 09:30:15    33.5
   2016-01-01 09:30:16    34.5
   2016-01-01 09:30:17    35.5
   2016-01-01 09:30:18    36.5
   2016-01-01 09:30:19    37.5
Name: C, dtype: float64
```
#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : 9d598a5e1eee26df95b3910e3f2934890d062caa
python           : 3.8.5.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19041
machine          : AMD64
processor        : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : English_United Kingdom.1252

pandas           : 1.2.1
numpy            : 1.19.2
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 20.3.3
setuptools       : 52.0.0.post20210125
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.20.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.6.0
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
numba            : None

</details>
