pd.Categorical(Series, categories=..) returns broken data with out-of-bound codes
#### Code Sample, a copy-pastable example if possible

```python
c1 = pd.Categorical(['A', 'B', 'B', 'C'])
# Works as expected:
#c2 = pd.Categorical(c1, categories=c1.categories[1:])
# Generates broken data:
c2 = pd.Categorical(pd.Series(c1), categories=c1.categories[1:])
print(c1[:2])
print(c2[:2])
print(c2.codes.max(), len(c2.categories))
```

Prints:

```python
[A, B]
Categories (3, object): [A, B, C]
[B, C]
Categories (2, object): [B, C]
2 2
```

#### Problem description

`pd.Categorical` should not be able to construct an object where `codes` is `>= len(categories)`. If it does this, then it's very likely that passing the array into other Pandas functions will trigger a segfault. For example, if you try to use this `Categorical` as a column in `pd.concat` then Python just dies:

```
>>> pd.concat([pd.DataFrame.from_dict({'A': c2}), pd.DataFrame.from_dict({'A': c2})], axis=0)

C:\>echo %ERRORLEVEL%
-1073741819
```

#### Expected Output

```python
[A, B]
Categories (3, object): [A, B, C]
[NaN, B]
Categories (2, object): [B, C]
1 2
```

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.24.1
pytest: 3.1.2
pip: 19.0.2
setuptools: 39.0.1
Cython: 0.27.2
numpy: 1.16.1
scipy: 1.2.1
pyarrow: 0.9.0
xarray: None
IPython: 6.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.3.0.dev0
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 3.8.0
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.11
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: 0.1.5
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
