pd.to_datetime errors args cause inconsistent behavior wrt to timestamp limitations
#### Code Sample

```python
df = pd.DataFrame({"date": ['10000101', '20180220']})

# Timestamp limitations correctly raise exception
pd.to_datetime(df.date, errors='raise')
...
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1000-01-01 00:00:00

# Timestamp limitations correctly coerce to NaT
pd.to_datetime(df.date, errors='coerce')
...
0          NaT
1   2018-02-20
Name: date, dtype: datetime64[ns]

# errors=`ignore` incorrectly results in datetime like object
pd.to_datetime(df.date, errors='ignore', format="%Y%m%d")
0    1000-01-01 00:00:00
1    2018-02-20 00:00:00
Name: date, dtype: object
```
#### Problem description

I believe that when `errors='ignore'`, and the timestamp limitations are violated by a datum, the result [should be the original input](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html) – not a datetime like object.

#### Expected Output

```python
pd.to_datetime(df.date, errors='ignore', format="%Y%m%d")
0    '10000101'
1    2018-02-20 00:00:00
Name: date, dtype: object
```

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

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.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: 0.21.0
pytest: 2.9.2
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
pyarrow: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
feather: None
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
