To solve this 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 amount of milligrams of potassium,
- \(x_2\) as the amount of milligrams of vitamin A.

The objective function is to maximize \(2.9x_1 + 5.37x_2\).

Constraints are as follows:
1. Immune support index: \(15x_1 + 7x_2 \geq 16\)
2. Energy stability index: \(14x_1 + 8x_2 \geq 42\)
3. Digestive support index: \(13x_1 + 17x_2 \geq 23\)
4. Muscle growth index: \(10x_1 + 2x_2 \geq 44\)
5. Cardiovascular support index: \(2x_1 + 12x_2 \geq 40\)
6. Additional constraint: \(-3x_1 + 2x_2 \geq 0\)
7. Immune support index upper bound: \(15x_1 + 7x_2 \leq 50\)
8. Energy stability index upper bound: \(14x_1 + 8x_2 \leq 126\)
9. Digestive support index upper bound: \(13x_1 + 17x_2 \leq 65\)
10. Muscle growth index upper bound: \(10x_1 + 2x_2 \leq 121\)
11. Cardiovascular support index upper bound: \(2x_1 + 12x_2 \leq 59\)

Symbolic representation:
```json
{
    'sym_variables': [('x1', 'milligrams of potassium'), ('x2', 'milligrams of vitamin A')],
    'objective_function': '2.9*x1 + 5.37*x2',
    'constraints': [
        '15*x1 + 7*x2 >= 16',
        '14*x1 + 8*x2 >= 42',
        '13*x1 + 17*x2 >= 23',
        '10*x1 + 2*x2 >= 44',
        '2*x1 + 12*x2 >= 40',
        '-3*x1 + 2*x2 >= 0',
        '15*x1 + 7*x2 <= 50',
        '14*x1 + 8*x2 <= 126',
        '13*x1 + 17*x2 <= 65',
        '10*x1 + 2*x2 <= 121',
        '2*x1 + 12*x2 <= 59'
    ]
}
```

Now, let's write the Gurobi code to solve this problem:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_potassium")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_A")

# Set the objective function
m.setObjective(2.9*x1 + 5.37*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(15*x1 + 7*x2 >= 16, "immune_support_index")
m.addConstr(14*x1 + 8*x2 >= 42, "energy_stability_index")
m.addConstr(13*x1 + 17*x2 >= 23, "digestive_support_index")
m.addConstr(10*x1 + 2*x2 >= 44, "muscle_growth_index")
m.addConstr(2*x1 + 12*x2 >= 40, "cardiovascular_support_index")
m.addConstr(-3*x1 + 2*x2 >= 0, "additional_constraint")
m.addConstr(15*x1 + 7*x2 <= 50, "immune_support_index_upper_bound")
m.addConstr(14*x1 + 8*x2 <= 126, "energy_stability_index_upper_bound")
m.addConstr(13*x1 + 17*x2 <= 65, "digestive_support_index_upper_bound")
m.addConstr(10*x1 + 2*x2 <= 121, "muscle_growth_index_upper_bound")
m.addConstr(2*x1 + 12*x2 <= 59, "cardiovascular_support_index_upper_bound")

# Optimize the model
m.optimize()

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