to_sql into MSSQL datetimeoffset column drops offset for DataFrame with one row
#### Problem description

(Prompted by [this Stack Overflow question](https://stackoverflow.com/q/59529424/2144390).)

When using `to_sql` with `mssql+pyodbc` and `fast_executemany=True`, uploading a DataFrame with a single row containing a tz-aware datetime into a `datetimeoffset` column causes the timezone offset to be lost. Doing the same thing with a DataFrame containing more than one row produces the correct result.

```python
from pprint import pprint
import sys
import urllib

import pandas as pd
import sqlalchemy as sa

print(sys.version)
# 3.8.1 (tags/v3.8.1:1b293b6, Dec 18 2019, 22:39:24) [MSC v.1916 32 bit (Intel)]

print(f'SQLAlchemy {sa.__version__}, pandas {pd.__version__}, pyodbc {pyodbc.version}')
# SQLAlchemy 1.3.12, pandas 0.25.3, pyodbc 4.0.28

connection_string = (
    r'DRIVER=ODBC Driver 17 for SQL Server;'
    r'SERVER=localhost,49242;'
    r'DATABASE=myDb;'
    r'Trusted_Connection=Yes;'
    r'UseFMTONLY=Yes;'
)
connection_uri = 'mssql+pyodbc:///?odbc_connect=' + urllib.parse.quote_plus(connection_string)
engine = sa.create_engine(connection_uri, fast_executemany=True)

# test environment
table_name = 'DateTimeOffset_Test'
engine.execute(sa.text(f"DROP TABLE IF EXISTS [{table_name}]"))
engine.execute(sa.text(f"CREATE TABLE [{table_name}] (id int primary key, dto datetimeoffset)"))

# test data
my_tz = datetime.timezone(datetime.timedelta(hours=-7))
dto_value = datetime.datetime(2020, 1, 1, 0, 0, 0, tzinfo=my_tz)
print(dto_value)  # 2020-01-01 00:00:00-07:00
#                                        ^

# test code
num_rows = 1
row_data = [(x, dto_value) for x in range(num_rows)]
df = pd.DataFrame(row_data, columns=['id', 'dto'])
print(df)
#    id                       dto
# 0   0 2020-01-01 00:00:00-07:00
#                            ^ - good
df.to_sql(table_name, engine, if_exists='append', index=False)
result = engine.execute(sa.text(f"SELECT id, CAST(dto as varchar(50)) AS foo FROM [{table_name}]")).fetchall()
pprint(result)
# [(1, '2020-01-01 00:00:00.0000000 +00:00')]
#                                     ^ - bad
```

Simply changing `num_rows = 1` to `num_rows = 2` produces correct results.

```python
# test code
num_rows = 2
row_data = [(x, dto_value) for x in range(num_rows)]
df = pd.DataFrame(row_data, columns=['id', 'dto'])
print(df)
#    id                       dto
# 0   0 2020-01-01 00:00:00-07:00
# 1   1 2020-01-01 00:00:00-07:00
#                            ^ - good
df.to_sql(table_name, engine, if_exists='append', index=False)
result = engine.execute(sa.text(f"SELECT id, CAST(dto as varchar(50)) AS foo FROM [{table_name}]")).fetchall()
pprint(result)
# [(0, '2020-01-01 00:00:00.0000000 -07:00'),
#  (1, '2020-01-01 00:00:00.0000000 -07:00')]
#                                     ^ - good
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.8.1.final.0
python-bits      : 32
OS               : Windows
OS-release       : 8.1
machine          : AMD64
processor        : AMD64 Family 18 Model 1 Stepping 0, AuthenticAMD
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : English_United States.1252

pandas           : 0.25.3
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 44.0.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : 0.9.3
psycopg2         : 2.8.4 (dt dec pq3 ext)
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : None
sqlalchemy       : 1.3.12
tables           : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None

</details>
