Series groupby does not include zero or nan counts for all categorical labels, unlike DataFrame groupby
#### Steps to reproduce

```
In [1]: import pandas

In [2]: df = pandas.DataFrame({'type': pandas.Categorical(['AAA', 'AAA', 'B', 'C']),
   ...:                        'voltage': pandas.Series([1.5, 1.5, 1.5, 1.5]),
   ...:                        'treatment': pandas.Categorical(['T', 'C', 'T', 'C'])})

In [3]: df.groupby(['treatment', 'type']).count()
Out[3]:
                voltage
treatment type
C         AAA       1.0
          B         NaN
          C         1.0
T         AAA       1.0
          B         1.0
          C         NaN

In [4]: df.groupby(['treatment', 'type'])['voltage'].count()
Out[4]:
treatment  type
C          AAA     1
           C       1
T          AAA     1
           B       1
Name: voltage, dtype: int64
```

#### Problem description
When performing a groupby on categorical columns, categories with empty groups should be present in output.  That is, the multi-index of the object returned by `count()` should contain the Cartesian product of all the labels of the first categorical column (`"treatment"` in the example above) and the second categorical column (`"type"`) by which the grouping was performed.

The behavior in cell [3] above is correct.  But in cell [4], after obtaining a `pandas.core.groupby.SeriesGroupBy` object, the series returned by the `count()` method does not have entries for all levels of the `"type"` categorical.

#### Expected Output
The output from cell [4] should be equivalent to this output, with length 6, and include values for the index values `(C, B)` and `(T, C)`.
```
In [5]: df.groupby(['treatment', 'type']).count().squeeze()
Out[5]:
treatment  type
C          AAA     1.0
           B       NaN
           C       1.0
T          AAA     1.0
           B       1.0
           C       NaN
Name: voltage, dtype: float64
```

#### Workaround

Perform column access after calling `count()`:
```
In [7]: df.groupby(['treatment', 'type']).count()['voltage']
Out[7]:
treatment  type
C          AAA     1.0
           B       NaN
           C       1.0
T          AAA     1.0
           B       1.0
           C       NaN
Name: voltage, dtype: float64
```

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

<details>

```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.5.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: en_US.UTF-8
LOCALE: None.None

pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.7
blosc: 1.5.0
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.3
html5lib: 0.999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
```

</details>
