BUG: groupby.rolling drops MultiIndex level
- [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
# prepare a dataframe where problem becomes obvious (details not important)

df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
                   'B': np.arange(40)})

df = df.reset_index().set_index(['index','A']).reorder_levels([1,0])
index_mapping = {a:(a-20)/10 for a in range(20,32)}
df = df.rename(index=index_mapping)
index_mapping = {a:(a-32)/10 for a in range(32,40)}
df = df.rename(index=index_mapping)
df = df.rename_axis(index={'index':'time'})



# groupby - rolling operation (here is the bug)

df.groupby('A').rolling(4).mean()

```

#### Problem description

Applying groupby.rolling on a multiindex dataframe should preserve the level that the rolling operation is applied to. Instead that index level is removed. This loses information and causes non-unique index values.

### **df**

<img width="97" alt="Screen Shot 2021-02-25 at 4 58 03 PM" src="https://user-images.githubusercontent.com/35338267/109225102-d2c12e80-778a-11eb-92b5-27e094b60c78.png">


### **df after applying groupby('A').rolling misses index level 'time'**

<img width="73" alt="Screen Shot 2021-02-25 at 4 58 09 PM" src="https://user-images.githubusercontent.com/35338267/109225082-cb9a2080-778a-11eb-9ee8-c76351852e71.png">


#### Expected Output

All index levels should be preserved. In older versions of pandas (1.1.2) the index levels were preserved, but there used to be a different bug: The whole multilevel index structure was duplicated, leading to twice the number of original levels. I guess that in an attempt to fix this, this new bug got introduced.


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

INSTALLED VERSIONS
------------------
commit           : 7d32926db8f7541c356066dcadabf854487738de
python           : 3.8.1.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.6.0
Version          : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : None.UTF-8

pandas           : 1.2.2
numpy            : 1.20.1
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 41.2.0
Cython           : 0.29.20
pytest           : 5.4.2
hypothesis       : None
sphinx           : 3.0.4
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.5.2
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.14.0
pandas_datareader: None
bs4              : 4.9.1
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.1
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.16.0
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : None
tables           : 3.6.1
tabulate         : 0.8.7
xarray           : 0.16.0
xlrd             : 1.2.0
xlwt             : None
numba            : 0.48.0


</details>
