Forecast - how to handle missing values in the dataset

I have a few questions regarding data preparation for Forecast.

I have a dataset with about 3,000 item_id's, the data is recorded on weekdays only (no row for weekends/holidays), and the forecast horizon is 1 day. For example:
[item_id | timestamp | target_value]
item_A | 2022-01-19 (Wed) | 100
item_A | 2022-01-20 (Thurs) | 101
item_A | 2022-01-21 (Fri) | 99
item_A | 2022-01-24 (Mon) | 98
item_A | 2022-01-25 (Tues) | 102

Q1. Is it recommended that the weekends (1/22, 1/23) row is inserted to the dataset with NaN as the target_value?

Q2. If target_value for a timestamp is NaN, do the RTS attributes get ignored regardless of what value it is?

Q3. When the Forecast is training, does Forecast recognize that on Friday, the next value to predict is on Monday rather than on Saturday?

Q4. If an item_id has small time series data points (for instance, global startend date ranges from 2018 to 2021, but a particular item_id only has data recorded for a couple months in 2020), should I front-fill & back-fill with NaN to match the global startend range? (I only intend to use the data for training, not creating a forecast)