Question about AlphaGo Zero's Neural network architecture?
This sounds like the t means time and the eight features are the latest 8 board states.

That is correct. The description in the article is clear.

I have the feeling the eight features should be the combination of rotated (4) and reflected (2) board states of the current board state. Please correct me if I am wrong.

That is wrong.
This data augmentation can be done by modifying state data stored from each episode, and applied during weight update step (either randomly or exhaustively), because any rotation/reflection of a board should have the same expected value. It is not necessary to provide that augmentation at the feature level.

Board repetition can be easily prevented by keeping tracking of all previous board states and when a new move is attempted, just compare against all previous board states.

A game engine can do that, as could bespoke code added to the agent that was specific to Go. However, a neural network cannot do that unless those previous states are part of the input.

The same is for komi.

If you want the board to correctly predict the value of a state, the nerual network needs to have the current player identity encoded in some way. Having it tracked outside of the neural network's inputs (i.e. outside of state representation) does not help with this.

What if the repetition happened to the last 9th board state?

Most typically in Go, the repetition rule has effects over a relatively short duration, because moves add stones in new positions. The relevant rules are called ko and superko, with superko being optional. When applying the standard ko rule, the repetition that it blocks is only likely to be achievable through play on short timescales (2 steps) that are covered by the AlphaGo Zero representation.
In the unlikely event that a play is blocked due to superko rule and repetition from earlier than 8 steps ago, the game engine would prevent a play that the agent may have assigned a high value to, and the agent could get the value wrong. I suspect that this has little or no impact on performance, as loops in Go board state of this duration are rare and do not occur much in practice.