Understanding one of the assumptions of linear regression: Multicollinearity
Interaction effect and Colinearity have two different meanings.
Multi-colinearity simply tells if two or more predictors are correlated i.e. change in one changes the other. I believe there is no confusion about this as you have also mentioned in your question.
But Multi-colinearity doesn't need the response variable for it to be figured out.
Interaction by definition, are always in the context of how predictors relate to the outcome.

More formally, two or more predictors are said to interact if their combined effect is different (less or greater) than what we would expect if we were to add the impact of each of their effects when considered alone.


A simple and intuitive example can be - consider the effects of water and fertilizer on the yield of a field corn crop. With no water but some fertilizer, the crop of field corn will produce no yield since water is a necessary requirement for plant growth. Conversely, with a sufficient amount of water but no fertilizer, a crop of field corn will produce some yield. However, the yield is best optimized with a sufficient amount of water and a sufficient amount of fertilizer. Hence water and fertilizer, when combined in the right amounts, produce a yield that is greater than what either would produce alone

It is represented as -
$y =β_0+β_1x_1+β_2x_2+β_3x_1x_2+error$
$β_3$ explains the interaction between $x_1$ and $x_2$
Interaction can be Additive (no interaction), Synergistic(+ve), Antagonistic(-ve), Atypical depending on different parameters values.
Reference
Quotes, example and the equation is from -
Feature Engineering and Selection: A Practical Approach for Predictive Models, Max Kuhn and Kjell Johnson
Good read for an in-depth explanation of different Feature engineering concepts.
Internet link