Pandas insert row into dataframe with timezone-aware index

I have a Dataframe that has a timezone-aware index, and when I try to insert a new row into this dataframe, it doesn't work, instead changing the type of the index to 'object' (ie, it can't add in the row into the current type)

MRE below:

import pandas as pd

df = pd.DataFrame({"time": ["2021/06/06 12:00:00"], "col1": [2]})
df.index = pd.to_datetime(df['time'])
df = df.drop('time', axis=1)
df.index = df.index.tz_localize('UTC')

# Doesn't work, index is now 'object' as this is considered a string
# row = pd.Series(name='2021/06/05 12:00:00')
# df = df.append(row)


# Also doesn't work, as timezones differ
row = pd.Series(name=pd.Timestamp('2021/06/05 12:00:00'))
df = df.append(row)

print(df.index)

I understand I can do the following:

tz = df.index[0].tz
row = pd.Series(name=pd.Timestamp('2021/06/05 12:00:00', tz=tz))

However I'm sceptical of whether this would work when the units differ, or maybe even some other property of a pandas Timestamp I don't know of, so would ideally like to completely copy over the Timestamp configuration of the index to the new timestamp I'm inserting.

If anyone would happen to know how to insert a new row into this dataframe whilst keeping the index type in tact that would be greatly appreciated

from Pandas insert row into dataframe with timezone-aware index