In short, the final ensemble is not a tree.
Consider this part of the Wikipedia article.
The $F_m(x)$ when $m=1$ is essentially a single tree $h_1(x)$ with its output multiplied by a learned number $\gamma_m$.
In the next iteration, you build $F_2(x)$ by using the previously created $F_1(x)$ and another initial tree $h_2(x)$.
This operation is repeated $m$ times, and then, for example if there were $m=100$ initial trees, you get your final ensemble, $F_{100}(x)=F_{99}(x)+F_{98}(x)+...+F_{1}(x) + \gamma_{100}h_{100}(x)$.
When you visualize a tree, you see the decision nodes. The output of the initial tree follows from its nodes.
However, the output of the final ensemble follows from a hundred of trees giving their outputs, then multiplying these outputs by the learned values and adding it all together.
Therefore, the final ensemble is not a tree, so you cannot visualize it as a tree. A tree is a decision graph, when a learned ensemble is a linear combination of what many trees decide to yield for a given input.
(Thanks to usεr11852 for clarifying my error. The comment section is about the previous version of the post before the fix.)