The double sum you are asking about can be considered to be the sum of all terms of the infinite array of numbers $a_{ij}$:
$$\begin{pmatrix}
a_{11} & a_{12} & \cdots & a_{1j} & \cdots\\
a_{21} & a_{22} & \cdots & a_{2j} & \cdots\\
\vdots& \vdots & &\vdots\\
a_{i1} & a_{i2} & \cdots & a_{ij} & \cdots\\
\vdots & \vdots & & \vdots \\
\end{pmatrix},$$
where the sum is taken with the particular order of first adding all elements of particular rows and then adding the resulting "row totals". The result you are citing claims that if all $a_{ij}$ is nonnegative, then it matters not in which order you add the entries of the infinite matrix, be that rows first and then row totals or columns first and then column totals.
I can't say your interpretation is right on the mark, because the issue is that even though one adds the entries of the same infinite array, the order of summation may result in summing the terms of different series in actuality. As the other answers point out this result holds essentially because we have no terms diminishing the total sum. Saying "both this iterated sums are rearrangements of the same series and hence converge to the same value, or diverge to infinity" really does not make any emphasis on this advantage, which I believe is sweeped under the phrase "same series".
I'd like to mention yet another (measure theoretical) interpretation of this result, which I recently encountered in Rudin's Real and Complex Analysis (p. 23). In the book Rudin gives this result as a corollary of Lebesgue Monotone Convergence Theorem applied to series of functions.
Theorem: Let $X$ be a measure space. If $\forall n: f_n:X\to [0,\infty]$ is measurable, then
$$\int_X \sum_n f_n d\mu= \sum_n \int_X f_n d\mu (\ast).$$
Corollary: Let $X:=\{x_1,x_2,...,x_n,...\}$ be a countable set and $\mu:\mathcal{M}_X:=\mathcal{P}(X)\to[0,\infty]$ be the counting measure:
$$\mu(E):=
\begin{cases}
|E|&, \mbox{ if} |E|<\infty\\
\infty&, \mbox{ if} |E|=\infty
\end{cases}.
$$
If $\forall i,j:a_{ij}\geq0$, then
$$\sum_i \sum_j a_{ij}=\sum_j \sum_i a_{ij}.$$
Proof:
- Set $\forall j: f_j:X\to[0,\infty], f_j(x):=\sum_i a_{ij}\chi_{\{x_i\}}(x)$; and $\forall i: \bar{f_i}:X\to[0,\infty], \bar{f_i}(x):=\sum_j a_{ij}\chi_{\{x_i\}}(x).$ Then $\sum_j f_j=\sum_i \bar{f_i}$. Indeed, let $x_{i_0}\in X$. Then
$$\sum_j f_j(x_{i_0})= \sum_j \sum_i a_{ij} \chi_{\{x_i\}}(x_{i_0})= \sum_j a_{{i_0}j},$$
and
$$\sum_i \bar{f_i}(x_{i_0})=\bar{f_{i_0}}(x_{i_0})=\sum_j a_{{i_0}j}.$$
- Observe that the (inner) sum over $i$ is the integral of $f_j$ and the (inner) sum over $j$ is the integral of $\bar{f_i}$. Then we have:
$$\sum_j\sum_i a_{ij}= \sum_j \int_X f_j d\mu\stackrel{(\ast)}{=}\int_X \sum_j f_j d\mu=\int_X \sum_i \bar{f_i}d\mu\stackrel{(\ast)}{=}\sum_i\int_X\bar{f_i}d\mu =\sum_i \sum_j a_{ij}.$$
Now consider the interpretation induced by the above discourse, viz., the (countable) combinations of characteristic functions behave in such a way that one can concentrate nonnegative "weights" of individual points. In the above proof $f_j$ puts a single weight on each point, while $\bar{f_i}$ concentrates all the weights of the point $x_i$.