Let $X_1,X_2,\dots,X_m$ be iid random variables uniformly distributed over the nonzero integers in $[-\Lambda,\Lambda]$, and let $S_m=X_1+\dots+X_m$. You want the probability that $S_m=0$, times $(2\Lambda)^m$.
As long as $m$ is large enough, then by the central limit theorem, $S_m/\sqrt{m} \approx N(0,\sigma^2)$, where $\sigma^2$ is the variance of $X_1$. Therefore, letting $\Phi(x)$ be the standard normal cdf, where $\Phi'(x)=(2\pi)^{-1/2}\exp(-x^2/2)$, we have
\begin{align}
P(S_m=0)
&=P\left(-\frac{1/2}{\sigma\sqrt m}<\frac{S_m}{\sigma\sqrt{m}}<\frac{1/2}{\sigma\sqrt m}\right)
\\&\approx\Phi\left(\frac{1/2}{\sigma\sqrt m}\right)-\Phi\left(\frac{1/2}{\sigma\sqrt m}\right)
\\&\approx\frac1{\sigma\sqrt{m}}\cdot \Phi'(0)
\\&=\frac1{\sigma\sqrt{2\pi m}}
\end{align}
Now, what is $\sigma$? Since $X_1$ is approximately a discrete uniform on the interval $[-\Lambda,\Lambda]$, which has variance $((\Lambda-(-\Lambda)+1)^2-1)/12$ (see Wikipedia: discrete uniform), it will be true that $\sigma\approx \Lambda/\sqrt{3}$. Therefore,
$$
\text{# of solutions}\approx \frac1{\Lambda \sqrt{\frac23 \pi m}}\cdot (2\Lambda )^m
$$
Furthermore, you can quantify the error in this approximation. The error in the approximating $S_m$ by a normal distribution is described by the Edgeworth series. Fortunately, since $X_1$ is symmetric and therefore has zero skewness, the error in this approximation is $O(1/m)$, so
$$
\text{# of solutions}=(2\Lambda )^m\left(\frac1{\Lambda \sqrt{\frac23 \pi m}}+O(1/m)\right)
$$
You can also given an exact answer to this problem in terms of a double sum of binomial coefficients. This may be useful if you want to verify the quality of the previous asymptotic answer.
Let us first count tuples without the constraint $k_n\neq 0$. If you add the quantity $\Lambda+1$ to each entry, you get a tuple of positive integers between $1$ and $2\Lambda+1$ summing to $m(\Lambda+1)$. Referring to here, the number of solutions is
$$
\sum_{i=0}^m (-1)^i\binom{m}i\binom{m(\Lambda+1)-(2\Lambda+1)i -1 }{m-1}
$$
Here, we use the convention that $\binom{r}k=0$ when $r$ is negative.
Now, to add back in the constraint $k_i\neq 0$, we use the principle of inclusion exclusion. Take all the solutions counted by the last equation, subtract the ones where some variable is zero, add back in the doubly subtract solutions, etc. The result is
$$
\sum_{k=0}^m (-1)^{m-k}\binom{m}k
\sum_{i=0}^k (-1)^i\binom{k}i
\binom{k(\Lambda+1)-(2\Lambda+1)i -1 }{k-1}
$$