## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of potassium' and 'milligrams of vitamin A'. Let's denote 'milligrams of potassium' as $x_1$ and 'milligrams of vitamin A' as $x_2$. The objective function to maximize is $2.9x_1 + 5.37x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
- $15x_1 + 7x_2 \geq 16$ (immune support index)
- $14x_1 + 8x_2 \geq 42$ (energy stability index)
- $13x_1 + 17x_2 \geq 23$ (digestive support index)
- $10x_1 + 2x_2 \geq 44$ (muscle growth index)
- $2x_1 + 12x_2 \geq 40$ (cardiovascular support index)
- $-3x_1 + 2x_2 \geq 0$
- $15x_1 + 7x_2 \leq 50$ (immune support index upper bound)
- $14x_1 + 8x_2 \leq 126$ (energy stability index upper bound)
- $13x_1 + 17x_2 \leq 65$ (digestive support index upper bound)
- $10x_1 + 2x_2 \leq 121$ (muscle growth index upper bound)
- $2x_1 + 12x_2 \leq 59$ (cardiovascular support index upper bound)

## 3: Provide the symbolic representation in JSON format
```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'
    ]
}
```

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

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

    # Define the variables
    x1 = model.addVar(name="milligrams_of_potassium", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="milligrams_of_vitamin_A", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

    # Define the objective function
    model.setObjective(2.9 * x1 + 5.37 * x2, gurobi.GRB.MAXIMIZE)

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

    # Optimize the model
    model.optimize()

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

optimize_problem()
```