= Solution
At the last observed failure time $a_*$, any individual still in the <risk set> must either fail there or be censored there or later. If there are no such censored observations, every remaining individual fails, so $d_*=r_*$. Thus the final <Kaplan–Meier estimator> factor is zero, while the final <Nelson–Aalen estimator> jump is one:
$$
\boxed{\widehat H_{\rm KM}(a_*)=+\infty,\qquad \widehat H_{\rm NA}(a_*)=\sum_{a_m\le a_*}\frac{d_m}{r_m}<\infty.}
$$
There is no small-jump approximation at this endpoint. Without ties the final <risk set> has size one, so the same discrepancy follows from its last single failure.
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