It is of course possible to construct a problem that will require strictly more steps than the most efficient algorithm for SAT; for example, we could give a language SAT-PRIME = {<φ,n>: φ is satisfiable and n is prime>, which will take longer to decide than SAT. (Primality can be checked by the Agrawal–Kayal–Saxena test in polynomial time.) But note that SAT-PRIME is polynomially reducible to SAT, so on the notion of hardness as given by polynomial reducibility, it is in fact just as hard.
Of course, there are other notions of hardness, but the question is whether those notions are actually useful. Not really, in this case. P is closed under many things; in particular, it is self-low—i.e., L ∈ P just in case L ∈ P^P, where A^B = {L: L is decidable in time A with access to an oracle for B deciding instances of B in one step}. Now being a bit more fine-grained is useful for problems we know to be in P—for example, the recent result on max flow.⁰ But when we’re dealing with exponential-time best-known worst-case complexities (which is the case for all NP-hard problems, since we have no proof yet that P = NP), plonking on a polynomial doesn’t seem so bad.