Series iteration and to_dict methods *sometimes* return underlying storage type vs. Python object
#### Code Sample, a copy-pastable example if possible

```python
import numpy as np
import pandas as pd

s1 = pd.Series({"a": np.int64(64), "b": 10})
for v in s1.to_dict().values():
    print(type(v))  # prints <class 'int'> 2x

s2 = pd.Series({"a": np.int64(64), "b": 10, "c": "ABC"})
for v in s2.to_dict().values():
    print(type(v))  # prints <class 'numpy.int64'> for first variable "a"

for k, v in s1.items():
    print(k, type(v))  # prints <class 'int'> 2x

for k, v in s2.items():
    print(k, type(v))  # prints <class 'numpy.int64'> again for the first variable "a"
```
#### Problem description
`pd.Series.to_dict` can return different types for objects depending on the composition of the series. This also affects iteration, e.g., `for k, v in series: ...`. This is inconsistent and, critically, leads to really weird and hard to debug issues downstream with types, especially around JSON conversion (the built-in `json` module and many others will blow up when it encounters numpy dtypes). 

I cannot find this exact issue open in the issue tracker, though there are a number of related issues including:
* An issue related to `DataFrame.to_dict` and inconsistent types (closed in `0.24`): https://github.com/pandas-dev/pandas/issues/24908
* This issue also related to scalar coercion on `DataFrame.to_dict` calls (also closed recently): https://github.com/pandas-dev/pandas/issues/23753
* This PR fixes the issue deriving from iteration, but it looks like the above case is either an untested edge case or a regression: https://github.com/pandas-dev/pandas/pull/17491

#### Expected Output
Expected output is for type coercion to Python ints to occur regardless of the exact column composition in the Series. https://github.com/pandas-dev/pandas/issues/24908 is a related issue for `DataFrame` coercions with irregular behavior happening as a result.

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

<details>

INSTALLED VERSIONS
------------------
```
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.24.2
pytest: None
pip: 10.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```

</details>
