[BUG]: Wrong unix timestamp parsing with floating point using pd.to_datetime
Hello!

I'm trying to parse floating point timestamps with `pd.to_datetime` but the only way that it works is using the **deprecated parameter** `coerce=True`

For example, without `coerce=True` the result is wrong.

``` python
>>> pd.to_datetime(1.1, unit='s')  # Expected result Timestamp('1970-01-01 00:00:01.100000')
Timestamp('1970-01-01 00:00:01')
```

However, with `coerce=True` the result is correct.

``` python
>>> pd.to_datetime(1.1, unit='s', coerce=True)  # Expected result Timestamp('1970-01-01 00:00:01.100000')
Timestamp('1970-01-01 00:00:01.100000')
```

I'm happy to help if you need more details.
#### Code Sample (copy-pastable example)

``` python
import pandas as pd
expected_result = pd.Timestamp('1970-01-01 00:00:01.100000')
timestamp1 = pd.to_datetime(1.1, unit='s')
timestamp2 = pd.to_datetime(1.1, unit='s', coerce=True)

print("Result without coerce: {}".format(timestamp1 == expected_result))
print("Result with coerce: {}".format(timestamp2 == expected_result))
```
#### pd.show_versions() output

``` python
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-65-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 19.2
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: None
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
```
