Your understanding of confounding and collinearity is correct. Note that in many contexts collinearity really refers to "perfect collinearity" where one variable is a linear combination of one or more other variables, but in some contexts it just refers to "high correlation" between variables.
Of course, in order for confounding to occur, there has to be a degree of correlation, though I would avoid saying "collinearity" due to the above.
interaction term: joint effect of independent variables (but doesn't this require correlation between those variables?)
A "joint effect" is a good way to undersdand it, but in no way does it require correlation between the variables. Consider an orthogonal factorial design experiment for example.
As another example we could also show this with a simple simulation of bivariate data where
X2 are uncorrelated yet a meaningful interaction exists:
> N <- 100
> X1 <- rnorm(N)
> X2 <- rnorm(N)
> cor(X1, X2)
 -0.0009943199 # X1 and X2 are uncorrelated
> Y <- X1 * X2 + rnorm(N)
> lm(Y ~ X1 * X2) %>% summary()
lm(formula = Y ~ X1 * X2)
Min 1Q Median 3Q Max
-2.92554 -0.43139 0.00249 0.65651 2.60188
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.03107 0.10439 0.298 0.767
X1 -0.03352 0.12064 -0.278 0.782
X2 -0.02822 0.10970 -0.257 0.798
X1:X2 0.76032 0.14847 5.121 1.57e-06 ***