Not only is that a bad way to term things but that's not even a statistical dead heat.
The MOE is just one side of a confidence interval and you seem to be applying it correct in one aspect. Candidate B's score is unlikely to be higher than 35.5%. Since it's usually a 95% confidence interval then Candidate B's score will be higher than 35.5% less than 5% of the time. Not impossible, just usually considered unlikely. On the other hand, a score of say 34% also has a cutoff probability. It's just not reported. It's probably something lower than random chance, we just don't know what it is from the MOE alone. Therefore, it's completely incorrect to say that scores with the MOE cutoff are all just randomly equally likely. Which is the implication of the assertion of statistical dead heat.
Not only that, you don't use overlapping confidence intervals that way. If you really wanted to only say that Candidate A was going to win if the probability of Candidate B winning was less than 5% then Candidate A is definitely in the lead. The lead is 8%. The confidence interval of that subtraction score is not double the confidence interval of the individual scores but only sqrt(2) times those scores. That's because finding the difference between the values would only double the variance. The confidence interval is based on a sqrt of the variance therefore combining them is the average (4.5) * sqrt(2). Since the MOE of your 8% lead is approximately 6.4% then Candidate A is in the lead.
As an aside, MOE's are very conservative and based on the 50% choice value. The formula is sqrt(0.25/n) * 2. There is a formula for calculating standard errors of difference scores that we could use as well. We would apply that using the found values rather than the 50% cutoff and that still gives us a significant lead for Candidate A (7.5% MOE). I believe that, given the questioners comment, and the proximity of that cutoff to the hypothetical one selected, that that was probably what they were looking for. I like my answer better though because, while not the most correct mathematically in this instance, it is the generally correct way to compare data where confidence intervals are reported for individual values and gives a framework for thinking about why the CI doesn't double.
Any introduction to both confidence intervals and to power would be helpful here. Even the wikipedia article on MOE looks pretty good.