pd.merge() on int+float produces object since 0.20
#### Code Sample, a copy-pastable example if possible

```python
In [2]: df1 = pd.DataFrame({'key': [1, 2], 'value': [0, 1]})

In [3]: df1.dtypes
Out[3]: 
key      int64
value    int64
dtype: object

In [4]: df2 = pd.DataFrame({'key': [1.0, 2.0], 'other_value': ['A', 'B']})

In [5]: df2.dtypes
Out[5]: 
key            float64
other_value     object
dtype: object

In [6]: print(pd.merge(df1, df2, how='left', on='key').dtypes)
key            object
value           int64
other_value    object
dtype: object
```
#### Problem description

I was expecting that in the merged DataFrame's "key" column pandas would either upcast ``int`` to ``float`` (like it does e.g. when missing values occur in an ``int`` column) or leave the column dtype as ``int``.

I checked and confirmed that the latter was the behaviour in pandas 0.19.2


#### Expected Output

Merging two DataFrames where the key column is of type ``int`` in the one and of type ``float`` in the other frame produces a key column of type ``int`` or ``float`` in the resulting frame (not ``object``).

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-81-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.21.0
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.5.0.post20170921
Cython: 0.26.1
numpy: 1.13.3
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.1.0
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.0
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
