as_index = False not working for applying groupby rolling agg to DataFrame
#### Problem description

When using a rolling agg function on a groupby object, we cannot ommit the groupby columns from the resulting dataframe's index using `as_index = False`, like we can when applying a non-rolling agg function.

#### Code Sample

```python
import pandas as pd

data = {
    'groupby_col': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', ],
    'agg_col': [1, 1, 0, 1, 0, 0, 0, 0, 1, 0],
}
df = pd.DataFrame(data)
df.groupby(['groupby_col'], as_index=False).rolling(4).agg({'agg_col': 'mean'})

```

#### Expected Output
```python
pd.DataFrame({'agg_col': {
  0: np.nan,
  1: np.nan,
  2: np.nan,
  3: 0.75,
  4: 0.5,
  5: np.nan,
  6: np.nan,
  7: np.nan,
  8: 0.25,
  9: 0.25}})
```

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>agg_col</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1</th>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2</th>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.75</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.50</td>
    </tr>
    <tr>
      <th>5</th>
      <td>NaN</td>
    </tr>
    <tr>
      <th>6</th>
      <td>NaN</td>
    </tr>
    <tr>
      <th>7</th>
      <td>NaN</td>
    </tr>
    <tr>
      <th>8</th>
      <td>0.25</td>
    </tr>
    <tr>
      <th>9</th>
      <td>0.25</td>
    </tr>
  </tbody>
</table>

#### Actual Output
```python
pd.DataFrame({'agg_col': {
  (0, 0): np.nan,
  (0, 1): np.nan,
  (0, 2): np.nan,
  (0, 3): 0.75,
  (0, 4): 0.5,
  (1, 5): np.nan,
  (1, 6): np.nan,
  (1, 7): np.nan,
  (1, 8): 0.25,
  (1, 9): 0.25}})
```

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th></th>
      <th>agg_col</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th rowspan="5" valign="top">0</th>
      <th>0</th>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1</th>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2</th>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.75</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.50</td>
    </tr>
    <tr>
      <th rowspan="5" valign="top">1</th>
      <th>5</th>
      <td>NaN</td>
    </tr>
    <tr>
      <th>6</th>
      <td>NaN</td>
    </tr>
    <tr>
      <th>7</th>
      <td>NaN</td>
    </tr>
    <tr>
      <th>8</th>
      <td>0.25</td>
    </tr>
    <tr>
      <th>9</th>
      <td>0.25</td>
    </tr>
  </tbody>
</table>

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : en_US.UTF-8

pandas           : 0.25.1
numpy            : 1.17.4
pytz             : 2019.3
dateutil         : 2.8.0
pip              : 19.3.1
setuptools       : 40.8.0
Cython           : 0.29.14
pytest           : 5.3.2
hypothesis       : None
sphinx           : 2.3.0
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : 1.0.1
pymysql          : None
psycopg2         : 2.8.4 (dt dec pq3 ext lo64)
jinja2           : 2.10.3
IPython          : 7.10.2
pandas_datareader: None
bs4              : 4.8.1
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.2
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.15.1
pytables         : None
s3fs             : None
scipy            : 1.3.0
sqlalchemy       : 1.3.11
tables           : 3.6.1
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None

</details>
