It implies only that the rows are dependent. When you perform row reduction, you do not change the row space (the space spanned by the rows of the matrix). Therefore, if you get a row of 0's in the reduction process, then the row space is spanned by fewer than $m$ elements, and therefore the original rows were dependent.
Even if the rows are dependent, the columns may or may not be dependent. Think of a tall $N$ by 1 matrix. The rows are dependent (if $N>1$), but there is only one column, so (assuming the column is not all zeroes), there is no column dependence.
Your procedures are rather non-standard in terms of finding basis for columnspaces and rowspaces. In particular your second procedure for finding the rowspace is not only unorthodox, but appears to be incorrect. To fully answer all of your questions takes a bit of time, and I apologize in advance for the length. The fact is, you can find both basis in a single shot by reducing the original matrix, say $A$, to its RREF, say $R$.
First of all, the RREF is fundamentally defined in terms of the rows. That's why it is the Reduced Row Echelon Form. Elementary row operations by design do not change the rowspace; each new row remains a linear combination of the old rows. That means you can row reduce your matrix all you want, but your rowspace remains the same. It follows that $R$ and $A$ share a rowspace.
But the rows of $R$ are linearly independent: each row has an entry, its leading pivot entry, that no other row has. The rows of $R$ clearly also span the rowspace of $R$, just by definition, and therefore it is a basis for the rowspace of $R$. Now since $\mathrm{row}(R) = \mathrm{row}(A)$, it follows that the rows of $R$ also form a basis for the rowspace of $A$. There is no need to go back to the original matrix because the rowspace has not changed between elementary row operations.
Finding a basis for the columnspace is a bit more complicated because elementary row operations does not preserve the columnspace. The columns of $R$ do not necessarily span the same space as the columns of $A$. Of course, the columnspace of $A$ is just the rowspace of $A^\mathrm{T}$ so we can always apply the previous procedure to $A^\mathrm{T}$. I believe this is what your second procedure tries to do, but it doesn't get it quite right. More on that later. In any case, reducing two matrices is hard work and luckily we can actually kill two birds with one stone.
If we encode the sequence of elementary row operations which take $A$ to $R$ as elementary matrices, then we can write
$$E_k\cdots E_1 A = R$$
where each $E_i$ is an elementary matrix. Let us just collectively write
$$EA = R$$
where $E$ is the product of all our elementary matrices. The important thing here is that $E$ is an invertible matrix since each $E_i$ is invertible. This fact actually characterizes row equivalent matrices: two (same sized) matrices $A$ and $B$ are row equivalent if and only if there exists an invertible matrix $E$ such that $A=EB$.
Let us write $\mathbf{a}_i$ as the $i$th column of $A$ and $\mathbf{r}_i$ as the $i$th column of $R$. By block matrix multiplication, it follows that
$$E\mathbf{a}_i = \mathbf{r}_i$$
My claim now is that linear relations are preserved between the columns of $A$ and $R$, i.e. for any set of scalar coefficients $\{c_i\}$, we have
$$\mathbf{0} = \sum_{i=1}^n c_i\mathbf{a}_i \iff \mathbf{0}=\sum_{i=1}^n c_i \mathbf{r}_i$$
The proof is simple:
$$\mathbf{0} = \sum_{i=1}^n c_i\mathbf{r}_i \iff \mathbf{0}=\sum_{i=1}^n c_iE\mathbf{a_i} \iff \mathbf{0}= E\left(\sum_{i=1}^n c_i\mathbf{a}_i\right) \iff \mathbf{0} = \sum_{i=1}^n c_i\mathbf{a}_i$$
The last line follows because $E$ is invertible and therefore its nullspace is just $\{\mathbf{0}\}$.
The above relations are very powerful and we have some immediate corollaries:
Let $\mathcal{A}=\{\mathbf{a}_{i_k}\}$ be a subset of $\{\mathbf{a}_i\}$ and let $\mathcal{R} = \{\mathbf{r}_{i_k}\}$ be the corresponding subset of $\{\mathbf{r}_i\}$. Then
$\mathcal{A}$ is linearly independent if and only if $\mathcal{R}$ is linearly independent.
$\mathcal{A}$ spans $\mathrm{col}(A)$ if and only if $\mathcal{R}$ spans $\mathrm{col}(R)$.
The two points above imply that $\mathcal{A}$ forms a basis for $\mathrm{col}(A)$ if and only if $\mathcal{R}$ forms a basis for $\mathrm{col}(R)$.
I will not prove the above propositions, the answer is getting a bit long and I think it would make a good exercise. I encourage you to attempt a proof yourself. The implications of the corollaries are straightforward: if you are given a basis for the columnspace of $R$, taking the corresponding vectors will give a basis for $A$. Taking the pivot columns of $R$ serves as an immediate basis for $\mathrm{col}(R)$ and the corresponding columns of $A$ serves as a basis for $\mathrm{col}(A)$. This is the underlying theory behind your first procedure.
To reiterate, take care to note that the pivot columns of $R$ do not form a basis for $\mathrm{col}(A)$ themselves, only the corresponding columns of $A$ do. Elementary row operations are designed to preserve the rowspace and not the columnspace. Oftentimes, $\mathrm{col}(A) \neq \mathrm{col}(R)$ and it makes no sense to take columns of $R$ to act as a basis for the columnspace of $A$. This is the fundamental reason why one of the procedures need to go back to the original matrix while the other one does not.
This now enables you to efficiently find the basis for the rowspace and columnspace all in one go. And we've explained how your first procedure works. With the benefit of hindsight, you should be able to see why your second procedure is a bit weird. You're row reducing the transpose, which means that you are essentially preforming elementary column operations on your original matrix. These operations do not preserve the rowspace and so the procedure as you've described it cannot be correct. At best, you are just preforming the procedure for finding the columnspace basis on $A^\mathrm{T}$, but that still requires you to go back to your original matrix. Even if your second procedure were correct, it turns out to be largely redundant as we've shown that simply reducing $A$ to its RREF $R$ is sufficient to find a basis for the columnspace and the rowspace (and the nullspace if you want).
Best Answer
If you know the determinant is multilinear (linear in each row/linear in each column), then express the the determinant as a sum of determinants of matrices that have repeated rows/columns. Then show that the determinant of a matrix with two identical rows/columns is always zero.
If you do not know the determinant is multilinear in the rows/columns, and you know the definition in terms of sums of products over all permutations, then the same argument works to show the determinant is a sum of determinants of matrices with repeated rows/columns.
If your only definition of determinant is inductively (by minors), then prove it by induction, expanding along a row or column that is not a linear combination of other rows/columns.
Edit: You've edited your question, and now the question is the precise converse of what you originally posted. This is a bit of bad form, since any answers already posted would automatically look completely wrong-headed. In the future, consider editing to add the new question to the old, indicating this is an edit or addition.
So, now you want to show that if the determinant is zero, then a row/column is a linear combination of other rows/columns. Looking at the tranpose if necessary it suffices to consider the case of rows. Note that applying elementary row operations does not change whether the determinant is zero or nonzero (an elementary row operation will either multiply the determinant by a nonzero constant, or will not change the determinant). So the determinant of the matrix is zero if and only if the determinant of its row-echelon form is zero; the determinant of the row-echelon form is zero if and only if there is a row of zeros. Tracing back the elementary row operations will express the original row as a linar combination of the other rows.