Mediator, Suppressor, Confounder in a Logistic Regression

I have a logistic model, say Category B vs Category A. I run a basemodel, with some controls and my variable of interest $$X_1$$ (continuous, standardised), which is slightly negative and non-significant.

Then in model $$2$$ I add variable $$X_2$$ (again, continuous and standardised), which is positive (beta coeff. $$+1.3$$) and significant, $$X_1$$ keeps being negative but with a much larger coefficient (beta coeff. from $$-0.1$$ to $$-0.7$$), which is now highly significant. (There is no change in the sample in moving from model $$1$$ to model $$2$$)
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$$X_2$$ is fairly correlated with $$X_1$$ ($$r= 0.45$$) in Category A (the baseline of the model), whereas in Category B the correlation is much smaller ($$r=0.10$$). Indeed there is a trend of $$X_2$$ among the various categories of the baseline, whereas among the "1s" its value is quite stable. Overall we have $$r=0.34$$
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And yes, $$X_2$$ could be on the causal pathway between $$X_1$$ and $$Y$$. Now I understand what is going on, I have more difficulties in labelling it, let's say. Maybe I am wrong, but I would rule out a confounding effect of $$X_2$$ between $$X_1$$ and $$Y$$, cause there is a causal pathway (something that should be missing in the case of a confounder). So I would go for a mediator effect. But does this apply also in case like that, where the effect of $$X_2$$ increases the magnitude of the relationship between $$X_1$$ and $$Y$$? Maybe is it a case of a suppressor?

You are correct that if $$X_2$$ is on the causal path between $$X_1$$ and $$Y$$ then it is a mediator, not a confounder, and if you are interested in the total causal effect of $$X_1$$ on $$Y$$ then you should not include $$X_2$$.
Regarding the situation where the inclusion of $$X_2$$ increases the magnitude of the $$X_1 \rightarrow Y$$ estimate, I can't see any reason why that is not plausible.