Default value for missing values on merge
This is a reopening of https://github.com/pandas-dev/pandas/issues/1836. The suggestion there was to add a parameter to `pd.merge`, such as `fillvalue`, whose value would be used instead of `NaN` for missing values. This isn't simply solved by `fillna` since adding `NaN` to columns casts them to `float`.

https://github.com/pandas-dev/pandas/issues/1836 also asked to provide an example where this would be useful. Admittedly, in my case there might be a simpler solution than `merge`, but anyway.

I have a `DataFrame` with a single column which is basically an index: it contains distinct numbers. I also have a `DataFrame` where one column contains some (but not all) values from the same index, while others contain useful data. I want to extend this `DataFrame` to include all values from the index, filling the other columns with zeros. I do this by calling

```python
pd.merge(df_with_index, smaller_df_with_data, on='col_index', how='outer').fillna(0)
```

and end up with a `DataFrame` where all columns except for `col_index` are cast to `float`.

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

<details>

```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.23-1-MANJARO
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.22.0
pytest: None
pip: None
setuptools: 38.5.1
Cython: 0.27.3
numpy: 1.14.0
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2018.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.1.2
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>
