BUG: int64 and uint64 values converted to float64 when concatenated
- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the latest version of pandas.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---

#### Code Sample

```python
import pandas as pd

pd.concat([
    pd.Series([1,2], dtype='uint64'),
    pd.Series([3,4], dtype='int64')
])

# Output:
0    1.0
1    2.0
0    3.0
1    4.0
dtype: float64
```

#### Problem description

The output datatype becomes float when the input datatypes are different integer types.

Since there are no NaN-values or decimals involved, it can be confusing that you suddenly get float output.

#### Expected Output
```py
0    1
1    2
0    3
1    4
dtype: int64
```
*union(int64, uint64) ⊆ int65, which is the same as int64 unless you have numbers close to "the edge".*

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.9.125-linuxkit
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : en_US.UTF-8
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.0
numpy            : 1.17.0
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.2.1
setuptools       : 41.0.1
Cython           : 0.29.13
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.5.0
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.7.0
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : None
fastparquet      : 0.3.2
gcsfs            : None
lxml.etree       : 4.5.0
matplotlib       : 3.1.1
numexpr          : 2.6.9
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.0
sqlalchemy       : 1.3.6
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None

</details>
