to_datetime() with errors=coerce and without return different values
#### Code Sample, a copy-pastable example if possible

Without errors='coerce'

```>>> import pandas as pd
>>> d = { 'ts': ['2019-02-02 08:07:13+00', '2019-02-02 08:03:22.54+00'] }
>>> df = pd.DataFrame(data=d)
>>> d
{'ts': ['2019-02-02 08:07:13+00', '2019-02-02 08:03:22.54+00']}
>>> df
                          ts
0     2019-02-02 08:07:13+00
1  2019-02-02 08:03:22.54+00
>>> df.dtypes
ts    object
dtype: object

>>> df['ts'] = pd.to_datetime(df['ts'], infer_datetime_format=True)
>>> df
                                ts
0        2019-02-02 08:07:13+00:00
1 2019-02-02 08:03:22.540000+00:00
>>> df.dtypes
ts    datetime64[ns, UTC]
dtype: object
```

With errors='coerce' 

```>>> import pandas as pd
>>> d = { 'ts': ['2019-02-02 08:07:13+00', '2019-02-02 08:03:22.54+00'] }
>>> df = pd.DataFrame(data=d)
>>> d
{'ts': ['2019-02-02 08:07:13+00', '2019-02-02 08:03:22.54+00']}
>>> df
                          ts
0     2019-02-02 08:07:13+00
1  2019-02-02 08:03:22.54+00
>>> df.dtypes
ts    object
dtype: object
>>> df['ts'] = pd.to_datetime(df['ts'], infer_datetime_format=True, errors='coerce')
>>> df
                   ts
0 2019-02-02 08:07:13
1                 NaT
```

#### Problem description

The functionality of to_datetime() with errors='coerce' is different than without. If I understand some of the other issues raised on this topic correctly, the functionality is different in some cases by design. In this case, the dates are very similiar, although different format.

#### Expected Output

```>>> import pandas as pd
>>> d = { 'ts': ['2019-02-02 08:07:13+00', '2019-02-02 08:03:22.54+00'] }
>>> df = pd.DataFrame(data=d)
>>> d
{'ts': ['2019-02-02 08:07:13+00', '2019-02-02 08:03:22.54+00']}
>>> df
                          ts
0     2019-02-02 08:07:13+00
1  2019-02-02 08:03:22.54+00
>>> df.dtypes
ts    object
dtype: object

>>> df['ts'] = pd.to_datetime(df['ts'], infer_datetime_format=True, errors='coerce')
>>> df
                                ts
0        2019-02-02 08:07:13+00:00
1 2019-02-02 08:03:22.540000+00:00
>>> df.dtypes
ts    datetime64[ns, UTC]
dtype: object
```

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

<details>

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

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.24.1
pytest: None
pip: 10.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.14.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 3.8.0
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.8.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
>>>

</details>
