Multiple features input in model

I have been recently working with tensorflow and neural works in general, and had a few concept questions.

Lets say I have a table where for every customer, there are X products columns, X associated dates for those products, X scores for each product, etc. From my understanding, this means that we have three features.

So customer 1 has [[A, 2022, 0.55], [B, 2021, 0.11], [B, 2022, 0.82]].

If I have a sample of 1m customers, how will that be used as an input? I have to first pad the everything to be equal size, so customer 1 will have the following sequence if we pad to 5 for example: [[A, 2022, 0.55], [B, 2021, 0.11], [B, 2022, 0.82], [0, 0, 0], [0, 0, 0]]

I’m using LSTM, so I have to reshape the input into [batch, timesteps, features] where batch is the sample size, timesteps is 5 (following the above example), and features is 3. Is that correct?

Also, if I encode the products into integers, it should be fine as such, without having to one-hot encode it, right?

One last thing; what is the difference in having the sequences as different features against having every feature as a separate input?