Pandas to_datetime() breaks on or after 2262-04-12
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
from datetime import datetime

# Works ok
dt = '2262-04-11'
datetime.strptime(dt, '%Y-%m-%d')

# Works ok
dt = '2262-04-11'
pd.to_datetime(dt, format='%Y-%m-%d')

# Works ok
dt = '2262-04-12'
datetime.strptime(dt, '%Y-%m-%d')

# Fails
dt = '2262-04-12'
pd.to_datetime(dt, format='%Y-%m-%d')
# ValueError: time data 2262-04-12 doesn't match format specified

# Fails
dt = '2262-04-12'
pd.to_datetime(dt)
# OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2262-04-12 00:00:00
```
#### Problem description

Any date on or after April 12th, 2262 seems to cause pd.to_datetime() to fail while datetime.strptime() works fine.

#### Expected Output

Output should match datetime.strptime()

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.23.4
pytest: 3.2.1
pip: 9.0.1
setuptools: 39.2.0
Cython: 0.26.1
numpy: 1.14.5
scipy: 1.1.0
pyarrow: None
xarray: 0.9.6
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: 1.0.1
sqlalchemy: 1.1.13
pymysql: None
psycopg2: 2.7.4 (dt dec pq3 ext lo64)
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: 0.2.1
pandas_datareader: None

</details>
