Surprising and inconsistent results when adding two `Series` which both contain duplicate labels.
If I add two `Series` objects, both of which contain the same duplicate label, it appears that I get a result in which every possible combination for that label appears. In the below: `s1` has the label `b` twice, `s2` has the label `b` thrice, and the result has the label `b` six times.

```python
Python 3.6.5 (default, Mar 29 2018, 15:37:32) 
[GCC 4.2.1 Compatible Apple LLVM 9.0.0 (clang-900.0.39.2)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> s1 = pd.Series(range(3), list('abb'))
>>> s2 = pd.Series(range(4), list('abbb'))
>>> s1 + s2
a    0
b    2
b    3
b    4
b    3
b    4
b    5
dtype: int64
```

That doesn't seem an unreasonable behaviour, but it's not applied consistently. If instead the second series has the same number of occurrences, then the data entries are added elementwise:
```python
>>> s3 = pd.Series(range(3), list('abb'))
>>> s4 = pd.Series(range(3), list('abb'))
>>> s3 + s4
a    0
b    2
b    4
dtype: int64
```

That seems to make the "outer product" behaviour in the first example somewhat dangerous, because any code that depends on it is at risk of giving inconsistent results if it happens to get a dataset where the numbers of the various labels match exactly. Should the second behaviour be altered to be consistent with the first? Or should maybe the first behaviour become an error (after a suitable deprecation period)?

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 17.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8

pandas: 0.22.0
pytest: None
pip: 10.0.0
setuptools: 39.0.1
Cython: 0.28.2
numpy: 1.14.2
scipy: 1.0.1
pyarrow: None
xarray: None
IPython: 6.3.1
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
