Groupby behaves differently when using levels and list of column keys
When grouping by several levels of a MultiIndex, groupby evaltuates all possible combinations of the groupby keys. When grouping by column name, it only evaluates what exist in the DataFrame. Also, this behavior does not exist in 0.14.1, but does in all final releases from 0.15.0 on.

This may be a new feature, not a bug, but I couldn't find anything in the [docs](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html#pandas.DataFrame.groupby), open or closed issues, etc. (closest was Issue #8138). If this is the intended behavior, it would be nice to have in the docs.

``` python
import pandas as pd
import numpy as np

df = pd.DataFrame(np.arange(12).reshape(-1, 3))
df.index = pd.MultiIndex.from_tuples([(1, 1), (1, 2), (3, 4), (5, 6)])
idx_names = ['x', 'y']
df.index.names = idx_names

# Adds nan's for (x, y) combinations that aren't in the data
by_levels = df.groupby(level=idx_names).mean()

# This does not add missing combinations of the groupby keys
by_columns = df.reset_index().groupby(idx_names).mean()

print by_levels
print by_columns

# This passes in 0.14.1, but not >=0.15.0 final
assert by_levels.equals(by_columns)
```
## INSTALLED VERSIONS

commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.15.2
nose: 1.3.4
Cython: 0.20.1
numpy: 1.9.1
scipy: 0.15.1
statsmodels: 0.7.0.dev-161a0f8
IPython: 2.3.0
sphinx: 1.2.2
patsy: 0.3.0
dateutil: 1.5
pytz: 2014.9
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.2
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
