BUG: surprising and possibly erroneous behavior of GroupBy.apply with an indexed series (index winds up duplicated)
- [x] I have checked that this issue has not already been reported.
  - I can't find anything in the bug tracker that matches the symptoms I'm reporting here, although it's a bit difficult to search (I'm not totally sure how to describe it)
- [x] I have confirmed this bug exists on the latest version of pandas.

---

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

```python
import pandas as pd

data = [{"label": l, "x" : x, "y": x + 1} for l in ("foo", "bar") for x in range(5)]
df = pd.DataFrame(data)
df = df.set_index(["label", "x"])
series = df["y"]
series2 = series.groupby(["label"]).apply(lambda s: s[2:])
print(series2.index)

# Output:

MultiIndex([('bar', 'bar', 2),
            ('bar', 'bar', 3),
            ('bar', 'bar', 4),
            ('foo', 'foo', 2),
            ('foo', 'foo', 3),
            ('foo', 'foo', 4)],
           names=['label', 'label', 'x'])
```

#### Problem description

The "label" field is duplicated in the index of the result

#### Expected Output

I expect the index after the apply to be the same as before, ie to only contain "label" once

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

<details>


INSTALLED VERSIONS
------------------
commit           : d9fff2792bf16178d4e450fe7384244e50635733
python           : 3.8.0.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.4.0-42-generic
Version          : #46-Ubuntu SMP Fri Jul 10 00:24:02 UTC 2020
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.1.0
numpy            : 1.19.1
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 42.0.1
Cython           : 0.29.15
pytest           : 5.4.0
hypothesis       : None
sphinx           : 2.2.1
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.4.2
html5lib         : None
pymysql          : None
psycopg2         : 2.8.4 (dt dec pq3 ext lo64)
jinja2           : 2.10.3
IPython          : 7.11.1
pandas_datareader: None
bs4              : 4.8.1
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.0
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : 1.5.2
sqlalchemy       : None
tables           : None
tabulate         : 0.8.6
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
