BUG: Wrong results and SegFaults when indexing with uint arrays
- [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, a copy-pastable example

```python
import pandas as pd
import numpy as np

ser = pd.Series([1, 2, 3])
arr8 = np.array([4], dtype=np.uint8)
arr16 = np.array([4], dtype=np.uint16)
arr32 = np.array([4], dtype=np.uint32)
arr64 = np.array([4], dtype=np.uint64)

print(ser[arr8])  # prints random number, probably out-of-bounds memory address
print(ser[arr16])  # prints random number, probably out-of-bounds memory address
print(ser[arr32])  # segfault
print(ser[arr64])  # correctly returns a KeyError
```

#### Problem description

Indexing into a Series without `.iloc` or `.loc` using an unsigned integer that points outside of the Series sometimes results in pandas returning some bogus value, and sometimes causes a segfault. I wasn't able to reproduce this behavior with signed integer arrays.

#### Expected Output

I assume that these queries should return a KeyError.

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

I confirmed the bug with the following two versions:

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.19.147-1-MANJARO
machine          : x86_64
processor        :
byteorder        : little
LC_ALL           : None
LANG             : en_IL.UTF-8
LOCALE           : en_IL.UTF-8

pandas           : 1.0.3
numpy            : 1.17.1
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.2.3
setuptools       : 41.2.0
Cython           : 0.29.19
pytest           : 5.1.2
hypothesis       : None
sphinx           : 2.4.4
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.5.0
html5lib         : None
pymysql          : 0.9.3
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.8.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.5.0
matplotlib       : 3.1.1
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.1.2
pyxlsb           : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : None
tables           : 3.6.1
tabulate         : None
xarray           : 0.15.1
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
numba            : 0.45.1

</details>

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.19.147-1-MANJARO
machine          : x86_64
processor        :
byteorder        : little
LC_ALL           : None
LANG             : en_IL.UTF-8
LOCALE           : en_IL.UTF-8

pandas           : 1.0.3
numpy            : 1.17.1
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.2.3
setuptools       : 41.2.0
Cython           : 0.29.19
pytest           : 5.1.2
hypothesis       : None
sphinx           : 2.4.4
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.5.0
html5lib         : None
pymysql          : 0.9.3
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.8.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.5.0
matplotlib       : 3.1.1
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.1.2
pyxlsb           : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : None
tables           : 3.6.1
tabulate         : None
xarray           : 0.15.1
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
numba            : 0.45.1

</details>
