Any Hypothesis Classes Other than Parity in Noisy PAC but not in SQ?
I think that the answer is "no", although I'm not certain and would also be interested in other examples. One thing that is known is that agnostic learning is significantly harder from an information theoretic perspective in the SQ model. Agnostically learning monotone disjunctions to error epsilon requires only $d/\epsilon^2$ examples in the PAC setting (though the task of learning might then be computationally hard...). In the SQ model, there is no agnostic learning algorithm for monotone disjunctions that has a polynomial dependence on $1/\epsilon$ in terms of the number of queries it makes, even ignoring computational considerations.