To tackle the problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints in terms of these variables.

Let's denote:
- $x_1$ as the milligrams of magnesium,
- $x_2$ as the milligrams of vitamin C.

The objective function is to maximize $7.49x_1 + 3.26x_2$.

The constraints given are:
1. The total combined energy stability index from milligrams of magnesium plus milligrams of vitamin C must be at least 23: $14x_1 + x_2 \geq 23$.
2. $10x_1 - 8x_2 \geq 0$.
3. The total combined energy stability index has to be less than or equal to 67: $14x_1 + x_2 \leq 67$.
4. Since the number of milligrams of vitamin C cannot be a fraction, we have $x_2 \in \mathbb{Z}$.

Thus, our symbolic representation in JSON format is:
```json
{
    'sym_variables': [('x1', 'milligrams of magnesium'), ('x2', 'milligrams of vitamin C')],
    'objective_function': '7.49*x1 + 3.26*x2',
    'constraints': [
        '14*x1 + x2 >= 23',
        '10*x1 - 8*x2 >= 0',
        '14*x1 + x2 <= 67'
    ]
}
```

Now, let's write the Gurobi code to solve this optimization problem. We will use Python as our programming language.

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define variables
x1 = m.addVar(lb=0, name="milligrams_of_magnesium")
x2 = m.addVar(vtype=GRB.INTEGER, lb=0, name="milligrams_of_vitamin_C")

# Set the objective function
m.setObjective(7.49*x1 + 3.26*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(14*x1 + x2 >= 23, "energy_stability_index_min")
m.addConstr(10*x1 - 8*x2 >= 0, "vitamin_c_constraint")
m.addConstr(14*x1 + x2 <= 67, "energy_stability_index_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of magnesium: {x1.x}")
    print(f"Milligrams of vitamin C: {x2.x}")
else:
    print("No optimal solution found")
```