difference between feature interactions and confounding variables
Some remarks (as far as I understand your questions):


In a causal model you need to reflect the "data generating process" (DGP). The DGP is a theoretical construct. You need to come up with an idea what is relevant for your research question, so what $X$ explain $y$ in a causal way. You can also include variables which are not so important. In tendence underspecification (exluding important variables) is a real problem. Overspecification can also be a problem but the consequences are less salient.
I think the wording is unclear here. In some fields "adjusting for confounders" means to "control" for all relevant variables (aka. confounders). First thing to do: get the wording right and understand what you mean when you say "confounders".
Where does this claim come from ("feature interaction"). In general, Logit is a model that is linear in the way you include variables, so if you think about interaction of two or more $x$ in your model, then no: Logit does not consider interaction unless you specify your model so to include interaction terms.
An easy way of thingking about interactions is the interaction of a continuous variable $x_1$ with a dummy variable (1 or 0) $x_2$. In the model $$y=\beta_0+\beta_1 x_1$$ you only have one intercept ($\beta_0$) and one slope ($\beta_1$). In a model with interaction between $x_1$ and $x_2$ you have $$ y=\beta_0+\beta_1 x_1 + \beta_2 x_2$$ where there is the same slope for $x_1$ but a different intercept in case $x_2=1$. Another way to think about interactions is to add "squared" terms because a squared term is just the interaction $x_1 * x_1$.
I don't understand this: needs a reference! I guess you speak about the inclusion of indicator variables (aka dummies) as interactions??!
No you can use any numbers as $x$.
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