To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints.

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

The objective function is to maximize: $4x_1 + 9x_2$

Given the problem description, we have the following constraints:

1. Cardiovascular support index for $x_1$: $7x_1$
2. Immune support index for $x_1$: $10x_1$
3. Energy stability index for $x_1$: $3x_1$
4. Cardiovascular support index for $x_2$: $3x_2$
5. Immune support index for $x_2$: $x_2$
6. Energy stability index for $x_2$: $13x_2$

And the constraints are:
- Total combined cardiovascular support index: $7x_1 + 3x_2 \geq 36$ and $7x_1 + 3x_2 \leq 103$
- Total combined immune support index: $10x_1 + x_2 \geq 40$ and $10x_1 + x_2 \leq 78$
- Total combined energy stability index: $3x_1 + 13x_2 \geq 70$ and $3x_1 + 13x_2 \leq 143$
- Additional constraint: $-10x_1 + 5x_2 \geq 0$

Now, let's represent this problem symbolically:

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

Given this symbolic representation, we can now write the Gurobi code to solve this optimization problem:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, name="grams_of_carbohydrates")
x2 = m.addVar(lb=0, name="milligrams_of_magnesium")

# Set the objective function
m.setObjective(4*x1 + 9*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(7*x1 + 3*x2 >= 36, "cardiovascular_support_index_min")
m.addConstr(7*x1 + 3*x2 <= 103, "cardiovascular_support_index_max")
m.addConstr(10*x1 + x2 >= 40, "immune_support_index_min")
m.addConstr(10*x1 + x2 <= 78, "immune_support_index_max")
m.addConstr(3*x1 + 13*x2 >= 70, "energy_stability_index_min")
m.addConstr(3*x1 + 13*x2 <= 143, "energy_stability_index_max")
m.addConstr(-10*x1 + 5*x2 >= 0, "additional_constraint")

# Optimize model
m.optimize()

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