Your calculation above works if A and B are independent, if A and B are not independent then the probability of both intervals containing the true combined parameters may be higher than or lower than the 0.81.
The individual (non-simultaneous, non-adjusted) confidence intervals answer a different question than the simultaneous ones, and both questions can be of interest at different times.
If the non adjusted C.I. does not include 0, but the adjusted or simultaneous one does include 0, how are you going to interpret that?
What all things do you include in your simultaneous intervals? should you adjust for the intercept? variables that could have been in the current model but you left out? If my main research question is about the relationship between x and y, but I include other variables to adjust for possibly lurking/confounding but I don't care about the "significance" of those other variables, should I include them in the adjustments?
My best guess for your question 1 is simplicity. The simple intervals are much simpler to compute (computers can generate reasonable ones automatically) but there are many ways to compute adjustments (Sheffe is just one) and deciding on which to use (and how to use it) depends on context, thought, and understanding that a computer should not be trusted to do automatically.
For 2. Those confidence intervals are valid for the question that they answer, it takes thought to determine if that is the question of interest or if some other routine needs to be used to give an appropriate answer to the question of interest.
For 3, the amount of adjustment depends on how they are related, being colinear is part of the relationship. This is another reason for not auto-adjusting everything, canned methods may get the adjustments wrong while giving people a false belief that everything has been properly adjusted.