Lets say you have a third variable C, which interacts as well with A. The model can be written:

    lm(Response ~ A + B + C + A:B + A:C)

When removing the main "A" variable, the model becomes:

    lm(Response ~ B + C + A:B + A:C)

In that case, the A effect does not entirely goes into the A:B interaction, but the A:C interaction might as well "absorb" part of this main A effect, so the estimates would change and the interpretation of terms would not be that easy. 

If the only issue is to calculate joint confidence intervals, it is possible for linear models, see this other SE topic:
http://stats.stackexchange.com/questions/96018/joint-confidence-interval