DataFrameGroupBy.filter returns empty dataframe when grouping by multiple columns and one column is type datetime64[ns]
There seems to be a bug in the `filter` method when grouping by multiple columns, where one of the grouping columns has type `datetime64[ns]`. Here is an example:

``` python
data = [
    ('a', pd.Timestamp('2015-08-01'), 0),
    ('a', pd.Timestamp('2015-08-01'), 0),
    ('a', pd.Timestamp('2015-08-02'), 1),
    ('b', pd.Timestamp('2015-08-01'), 2),
    ('b', pd.Timestamp('2015-08-02'), 3),
    ('c', pd.Timestamp('2015-08-01'), 4),
    ('c', pd.Timestamp('2015-08-02'), 5),
    ('c', pd.Timestamp('2015-08-02'), 5),
]
data = pd.DataFrame(data, columns=('A', 'B', 'group_number'))
groups = data.groupby(['A', 'B'])
assert len(groups) == 6  # OK

filtered = groups.filter(lambda x: len(x) > 1)
assert len(filtered) == 4  # AssertionError
```

Now, if I change the timestamps to strings, `filter` works as expected:

``` python
data2 = data.copy()
data2['B'] = data2['B'].astype(str)
groups2 = data2.groupby(['A', 'B'])
filtered2 = groups2.filter(lambda x: len(x) > 1)
assert len(filtered2) == 4  # OK
```

Also, `filter` works as expected when grouping by _only_ column B, regardless of whether it contains timestamps or strings.

What's going on here? Am I missing something or is this actually a bug? Thanks!
