## Step 1: Define the symbolic representation of the variables
The variables are 'grams of carbohydrates' and 'milligrams of magnesium', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$: grams of carbohydrates
- $x_2$: milligrams of magnesium

## Step 3: Define the objective function in symbolic notation
The objective function to maximize is $4x_1 + 9x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $7x_1 + 3x_2 \geq 36$
- $10x_1 + x_2 \geq 40$
- $3x_1 + 13x_2 \geq 70$
- $-10x_1 + 5x_2 \geq 0$
- $7x_1 + 3x_2 \leq 103$
- $10x_1 + x_2 \leq 78$
- $3x_1 + 13x_2 \leq 143$

## 5: Provide the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'grams of carbohydrates'), ('x2', 'milligrams of magnesium')],
'objective_function': '4*x1 + 9*x2',
'constraints': [
    '7*x1 + 3*x2 >= 36',
    '10*x1 + x2 >= 40',
    '3*x1 + 13*x2 >= 70',
    '-10*x1 + 5*x2 >= 0',
    '7*x1 + 3*x2 <= 103',
    '10*x1 + x2 <= 78',
    '3*x1 + 13*x2 <= 143'
]
}
```

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

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=0)  # grams of carbohydrates
    x2 = model.addVar(name="x2", lb=0)  # milligrams of magnesium

    # Define the objective function
    model.setObjective(4 * x1 + 9 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(7 * x1 + 3 * x2 >= 36, name="cardiovascular_support_index")
    model.addConstr(10 * x1 + x2 >= 40, name="immune_support_index")
    model.addConstr(3 * x1 + 13 * x2 >= 70, name="energy_stability_index")
    model.addConstr(-10 * x1 + 5 * x2 >= 0, name="balance_constraint")
    model.addConstr(7 * x1 + 3 * x2 <= 103, name="cardiovascular_support_index_upper_bound")
    model.addConstr(10 * x1 + x2 <= 78, name="immune_support_index_upper_bound")
    model.addConstr(3 * x1 + 13 * x2 <= 143, name="energy_stability_index_upper_bound")

    # Optimize the model
    model.optimize()

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

optimize_problem()
```