The question concerns maximizing the likelihood, which is a function of the sole parameter $\rho \in [-1,1]$, by finding its critical points. Among them are zeros of the derivative of the log likelihood. In this case, the derivative of the log likelihood is a rational function of $\rho$: the numerator is a cubic polynomial and the denominator is a quartic polynomial (equal to $(1-\rho^2)^2$). Since any cubic polynomial may have up to three real roots, how can we be assured there is a unique maximum of the likelihood?
Well, we cannot: as Lehmann points out, there may be two local maxima. But no more than that. The reason is that whenever there are two (anywhere among the real numbers, not just in the range $[-1,1]$) then--because any polynomial is differentiable--there must be a local minimum lying between them. That local minimum would correspond to a third root, and that's all there can be.
This argument can be appreciated by looking at graphs of some of the functions that actually show up. In both figures, the blue dotted curve plots the log likelihood, the red dashed curve plots its derivative, and the solid gold curve is the numerator of the derivative--the cubic polynomial in the question.
(This situation can only arise when both $\sum x_i^2$ and $\sum y_i^2$ are appreciably less than $n$, which is increasingly unlikely as $n$ grows large. In this figure, both values are $0.08 n$.)
You can see two local maxima of the log likelood, one near each end. Between them, therefore, there must be a local minimum--and it is apparent at the bottom of the shallow "U" curve near $\rho=0$. Three zeros of the derivative and its numerator are visible where the red and gold curves cross the $\rho$ axis: one of them is very close to $0$, one is negative, and the other positive. (This is a situation where the data are so inconsistent with the model that the maximum likelihood method will not do a good job at estimating $\rho$, which in this case is $0.13$.)
The next figure is more realistic. The value of $\rho$ is the same but this time $\sum x_i^2 = n = \sum y_i^2$. The derivative has exactly one real zero and (therefore) the likelihood has at most one local maximum within the interior of the domain, $(-1,1)$.
Note that $\pm 1$ are always critical points (the derivative becomes unbounded as it approaches these endpoints) and always need to be considered. However, it's not hard to show that they will never be local maxima unless the data are perfectly correlated or perfectly anticorrelated.