API: Preferred MultiIndex result for groupby().rolling() for an object with a MultiIndex
`groupby().rolling()` in master currently constructs the resulting `MultiIndex` manually by inserting `groupby` keys as the first level(s) and then the original object's `Index` as the second level(s).

However, `groupby().rolling()` behaves similarly to `groupby().transform()` (i.e. maintains the original shape), so should the resulting index align with results of `groupby().transform()`?

```
In [4]: pd.__version__
Out[4]: '1.3.0.dev0+238.g1d196295c'

In [6]: df = pd.DataFrame({'a': [1], 'b': [2]})

In [7]: df
Out[7]:
   a  b
0  1  2

# Example of the original DataFrame having a regular Index
In [8]: df.groupby('a').rolling(1).sum()
Out[8]:
       a    b
a
1 0  1.0  2.0

In [16]: df.groupby('a').transform(lambda x: np.sum(x))
Out[16]:
   b
0  2

In [9]: df = pd.DataFrame({'a': [1], 'b': [2]}, index=pd.MultiIndex.from_tuples([('idx1', 'idx2')], names=['label1', 'label2']))

In [9]: df
Out[9]:
               a  b
label1 label2
idx1   idx2    1  2

# Examples of the original DataFrame having a MultiIndex
In [10]: df.groupby('label1').rolling(1).sum()
Out[10]:
                        a    b
label1 label1 label2
idx1   idx1   idx2    1.0  2.0

In [20]: df.groupby('label1').transform(lambda x: np.sum(x))
Out[20]:
               a  b
label1 label2
idx1   idx2    1  2

In [11]: df.groupby('a').rolling(1).sum()
Out[11]:
                   a    b
a label1 label2
1 idx1   idx2    1.0  2.0

In [21]: df.groupby('a').transform(lambda x: np.sum(x))
Out[21]:
               b
label1 label2
idx1   idx2    2

In [12]: df.groupby(['label1', 'a']).rolling(1).sum()
Out[12]:
                          b
label1 a label1 label2
idx1   1 idx1   idx2    2.0

In [22]: df.groupby(['label1', 'a']).transform(lambda x: np.sum(x))
Out[22]:
               b
label1 label2
idx1   idx2    2
```


As shown, when the original object as a `MultiIndex`, there is consistency of the resulting `MultiIndex` for the `groupby().rolling()` result but can lead to redundancy. There is lack of consistency of the resulting `MultiIndex` for the `groupby().transform()` result but looks more convenient.

IMO I prefer the consistent result we have today in `groupby().rolling()` but open to thoughts.

