Decimal fields dropped in group by with more than one column
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
import decimal

df = pd.DataFrame({'a': [decimal.Decimal('4.56')]*6, 'b': range(3, 6)*2, 'c': range(6)})
print df.groupby('b')['a'].sum()
print df.groupby('b')['a', 'c'].sum()
print df.groupby('b').agg({'a': 'sum', 'c': 'sum'})
```

Output:
```
b
3    9.12
4    9.12
5    9.12
Name: a, dtype: object
   c
b
3  3
4  5
5  7
      a  c
b
3  9.12  3
4  9.12  5
5  9.12  7
```

#### Problem description

The aggregation over column a is dropped when another field is accessed from the groupby object, but works when requested through `agg` (I'm not sure if 'sum' is exactly equivalent to `.sum()` in the above).

#### Expected Output

```
b
3    9.12
4    9.12
5    9.12
Name: a, dtype: object
      a  c
b
3  9.12  3
4  9.12  5
5  9.12  7
      a  c
b
3  9.12  3
4  9.12  5
5  9.12  7
```

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

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.23.4
pytest: None
pip: 18.0
setuptools: 39.1.0
Cython: None
numpy: 1.14.3
scipy: 1.1.0
pyarrow: 0.8.0
xarray: None
IPython: 5.7.0
sphinx: None
patsy: 0.5.0
dateutil: 2.7.2
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: 1.0.4
lxml: None
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: None
psycopg2: 2.7.4 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
