Series binary operator behavior depends on the count of each value in the index
#### Code Sample

```python
>>> one = pd.Series([12, 13, 14], index=[3, 3, 3])
>>> other = pd.Series([-20, 20], index=[3, 3])
>>> another = pd.Series([-20, 20, 40], index=[3, 3, 3])
>>> print(one.subtract(other))
3    32
3    -8
3    33
3    -7
3    34
3    -6
dtype: int64
>>> print(one.subtract(another))
3    32
3    -7
3   -26
dtype: int64
```
#### Problem description

This behavior leads to output series length that depends on the number of duplicate index values. In data where this value is not known, the number of rows in the output is unpredictable.

#### Expected Output

I would expect there to be an option in `pd.Series.subtract` allowing the user to determine the operation's behavior with respect to repeated indices, something like the following:

```python
>>> one.subtract(another, repeat=True)
3    32
3    -8
3    -28
3    33
3    -7
3    -27
3    34
3    -6
3    -26
dtype: int64
>>> one.subtract(another, repeat=False)
3    32
3    -7
3   -26
dtype: int64
>>> # when the number of rows with index 3 is the same in both Series:
... one.subtract(other, repeat=True)
3    32
3    -8
3    33
3    -7
3    34
3    -6
dtype: int64
>>> one.subtract(other, repeat=False)
3    32
3    -7
3   NaN
dtype: int64
```

If this is what `level` is for, I have no idea what the documentation is saying.

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

<details>

```
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Linux
OS-release       : 3.10.0-693.21.1.el7.x86_64
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.0
numpy            : 1.16.4
pytz             : 2019.1
dateutil         : 2.8.0
pip              : 19.1.1
setuptools       : 41.0.1
Cython           : 0.29.12
pytest           : 5.0.1
hypothesis       : None
sphinx           : 2.1.2
blosc            : None
feather          : None
xlsxwriter       : 1.1.8
lxml.etree       : 4.3.4
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.7.0
pandas_datareader: None
bs4              : 4.7.1
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.3.4
matplotlib       : 3.1.0
numexpr          : 2.6.9
odfpy            : None
openpyxl         : 2.6.2
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.0
sqlalchemy       : 1.3.5
tables           : 3.5.2
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.1.8
```

</details>
