1. Finding approximate or near-optimal solutions for hard optimization problems is almost always easy. So in this case, an amoeba did something that is also easy to do with a classical computer.
2. 8 is a small number. There are 8! possible paths, which is about 40,000, which means you could find the optimal solution to this problem in a millisecond or two with a classical computer. To be remotely interesting, you'd need to solve a TSP for a number like, say, 50 or 100.
3. Even if you had an amoeba that somehow could find solutions for large numbers of cities, how would you know? You can't solve the problem instance yourself, so you don't know how close it is to the optimum. This is because TSP is not in NP, which are the set of problems whose solutions can be verified in polynomial time. Rather, its NP-hard, meaning its at least as hard as any NP problem, but its solutions can't be verified in polynomial time. So if amoebas had some magic TSP solving algorithm, we probably wouldn't be able to tell that they did unless we had our own magic algorithm to test it against.
4. Also, even if the amoeba could solve this optimally for large instances, _most_ instances of hard problems are actually easy. Therefore, simply showing that the amoebas can do this for some finite number of instances is not sufficient. An efficient algorithm for TSP or some other hard problem has to be able to solve _any possible instance_ efficiently, in order for this to have any implications for questions like P=NP. So if you wanted to show that amoebas had a magic TSP algorithm, you'd have to formally prove that their algorithm works on all instances. Since amoebas themselves are not easily formalizable, I don't see how you'd do this.