Entropy and Information Gain for multi attributes
You can group attributes together, but (assuming you're building some kind of decision tree) your decision tree will no longer be a binary tree, and you probably won't be any better off than just splitting by one attribute. Say for example you group $x_1$ and $x_2$ together, then there are 4 possible values for this new meta-attribute. You can use the same entropy calculation for this meta-attribute, and split on these four values.

But ask yourself this: why would you want to split on multiple attributes at once? Sure, it's true that $y=1$ whenever $x_1$, $x_2$, and $x_3$ are 1, but once you split on this you can't split on any subset of these three (for example, observe that $y=0$ whenever $x_2$ and $x_3$ are zero. If we split on all three variables we'd need two identical subtrees, one for $x_1=1, x_2=0, x_3=0$ and one for when they're all zero.)