Series construction: _try_cast typo, function often taking slow route?
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
from pandas.core.internals.construction import _try_cast

arr = np.arange(0, 10, dtype = "int64")

# should return early
_try_cast(arr, None, False, False)
```
#### Problem description

During series construction, a function, `sanitize_array` attempts to use `_try_cast`, to cast the input to a better type. `_try_cast` is fairly slow to run, so it tries to avoid casting in common cases. However, due to a missing `not` keyword, it appears `_try_cast` **runs for the cases it wants to avoid (like the one above)**.

Here are the relevant lines of `_try_catch`:

https://github.com/pandas-dev/pandas/blob/master/pandas/core/construction.py#L511-L513

Should this be `not maybe_castable(arr)`? 

It is very surprising that lines like this would intentially create an array, and then try to cast it, even when the dtype option passed is None.

https://github.com/pandas-dev/pandas/blob/master/pandas/core/construction.py#L429-L432

#### Expected Output

`_try_cast` not run during `sanitize_array` for common types (e.g. int64). However, from looking at it with `%%prune`, and running pdb, I can see functions like `maybe_cast_to_datetime` are called.

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.7.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 17.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.1
numpy            : 1.16.1
pytz             : 2018.9
dateutil         : 2.8.0
pip              : 19.1.1
setuptools       : 39.0.1
Cython           : None
pytest           : 4.4.2
hypothesis       : None
sphinx           : 2.0.1
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : 0.9.3
psycopg2         : 2.8.2 (dt dec pq3 ext lo64)
jinja2           : 2.10.1
IPython          : 7.5.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.0
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.2.1
sqlalchemy       : 1.3.4
tables           : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None

</details>
