Space and Time Complexity

The time and space complexities are not related to each other. 
They are used to describe how much space/time your algorithm takes based on the input.

For example When the algorithm has space complexity of:

O(1) - constant - the algorithm uses a fixed (small) amount of space which
doesn't depend on the input. For every size of the input the algorithm will take
the same (constant) amount of space. This is the case in your example as the input
is not taken into account and what matters is the time/space of the print command.

O(n), O(n^2), O(log(n))... - these indicate that you create additional objects based 
on the length of your input. For example creating a copy of each object of v storing
it in an array and printing it after that takes O(n) space as you create n additional objects.

Your example is an example that time and space complexity might be different. 
It takes v.length * print.time to print all the elements. But the space is always
the same - O(1) because you don't create additional objects.

In contrast the time complexity describes how much time your algorithm consumes based
on the length of the input. Again:

O(1) - no matter how big is the input it always takes a constant time - for example only one 
instruction. Like

function(list l) { print("i got a list"); }

O(n), O(n^2), O(log(n)) - again it's based on the length of the input. For example

function(list l) {
    for(node in l) {
        print(node);
    }
}
Note that both last examples take O(1) space as you don't create anything. Compare it to

function(list l) {
    list c;
    for(node in l) {
        c.add(node);
    }
}
which takes O(n) space