## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of magnesium' and 'milligrams of vitamin C', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $7.49x_1 + 3.26x_2$.

## 3: Define the constraints in symbolic notation
The constraints given are:
1. $14x_1 + 1x_2 \geq 23$
2. $10x_1 - 8x_2 \geq 0$
3. $14x_1 + 1x_2 \leq 67$
4. Specifically, $14x_1 + 1x_2 = 67$ is not a constraint but an upper bound which is already considered.
5. $x_1$ can be a non-whole number.
6. $x_2$ cannot be a fraction.

## 4: Consider the integer constraint for $x_2$
Since $x_2$ represents milligrams of vitamin C and cannot be a fraction, we will consider $x_2$ as an integer variable.

## 5: Formulate the optimization problem in Gurobi
We will use Gurobi to solve this optimization problem. The problem can be formulated as:

## Step 6: Write down the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'milligrams of magnesium'), ('x2', 'milligrams of vitamin C')],
'objective_function': '7.49*x1 + 3.26*x2',
'constraints': [
    '14*x1 + 1*x2 >= 23',
    '10*x1 - 8*x2 >= 0',
    '14*x1 + 1*x2 <= 67'
]
}
```

## 7: Implement the optimization problem using Gurobi
```python
import gurobi

def solve_optimization_problem():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="milligrams_of_magnesium", lb=-float('inf'), ub=float('inf'))
    x2 = model.addVar(name="milligrams_of_vitamin_C", lb=-float('inf'), ub=float('inf'), vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(7.49 * x1 + 3.26 * x2, gurobi.GRB.MAXIMIZE)

    # Define the constraints
    model.addConstr(14 * x1 + 1 * x2 >= 23)
    model.addConstr(10 * x1 - 8 * x2 >= 0)
    model.addConstr(14 * x1 + 1 * x2 <= 67)

    # Optimize the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of magnesium: {x1.varValue}")
        print(f"Milligrams of vitamin C: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```