BUG: Replacing rows with iloc changes dtype
- [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.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

#### Code Sample, a copy-pastable example

```python
import pandas as pd

df1 = pd.DataFrame([(1, pd.Timestamp.utcfromtimestamp(1))] * 5, columns=['nb', 'ts'])
df2 = pd.DataFrame([(2, pd.Timestamp.utcfromtimestamp(2))] * 3, columns=['nb', 'ts'])

print("df2:\n", df2)
print("\ndf1 before:\n", df1)

df1.iloc[[1, 4]] = df2.iloc[[0, 2]].values

print("\ndf1 after replacing some rows by some of df2's rows:\n", df1)
```

Full output is
```
df2:
    nb                  ts
0   2 1970-01-01 00:00:02
1   2 1970-01-01 00:00:02
2   2 1970-01-01 00:00:02

df1 before:
    nb                  ts
0   1 1970-01-01 00:00:01
1   1 1970-01-01 00:00:01
2   1 1970-01-01 00:00:01
3   1 1970-01-01 00:00:01
4   1 1970-01-01 00:00:01

df1 after replacing some rows by some of df2's rows:
    nb                   ts
0   1           1000000000
1   2  1970-01-01 00:00:02
2   1           1000000000
3   1           1000000000
4   2  1970-01-01 00:00:02
```

#### Problem description

The rows which are _not_ overwritten have their dtype changed (cast from Timestamp to int) although all dtypes align.

#### Expected Output

```
df1 after replacing some rows by some of df2's rows:
  nb                  ts
0  1 1970-01-01 00:00:01
1  2 1970-01-01 00:00:02
2  1 1970-01-01 00:00:01
3  1 1970-01-01 00:00:01
4  2 1970-01-01 00:00:02
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.7.final.0
python-bits      : 64
OS               : Linux
OS-release       : 3.10.0-862.14.4.el7.x86_64
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           : 1.0.3
numpy            : 1.14.5
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.2.1
setuptools       : 39.1.0
Cython           : 0.29.13
pytest           : 5.0.1
hypothesis       : None
sphinx           : 2.1.2
blosc            : None
feather          : None
xlsxwriter       : 1.1.8
lxml.etree       : 4.4.0
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.13.0
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.0
matplotlib       : 3.1.1
numexpr          : 2.6.9
odfpy            : None
openpyxl         : 2.6.2
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.0.1
pyxlsb           : None
s3fs             : None
scipy            : 1.3.0
sqlalchemy       : 1.3.6
tables           : 3.5.2
tabulate         : 0.8.3
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.1.8
numba            : 0.45.1

</details>
