## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'grams of protein' and 'grams of carbohydrates'. Let's denote 'grams of protein' as $x_1$ and 'grams of carbohydrates' as $x_2$. The objective function to minimize is $9x_1 + 4x_2$. The constraints are:
- The immune support index of $x_1$ is 5.39.
- The cardiovascular support index of $x_1$ is 4.14.
- The immune support index for $x_2$ is 7.75.
- The cardiovascular support index for $x_2$ is 0.59.
- $5.39x_1 + 7.75x_2 \geq 26$
- $4.14x_1 + 0.59x_2 \geq 19$
- $-8x_1 + 3x_2 \geq 0$
- $5.39x_1 + 7.75x_2 \leq 30$
- $4.14x_1 + 0.59x_2 \leq 54$

## Step 2: Convert the problem into a symbolic representation
The symbolic representation of the variables is:
- $x_1$ for 'grams of protein'
- $x_2$ for 'grams of carbohydrates'

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

The constraints are:
1. $5.39x_1 + 7.75x_2 \geq 26$
2. $4.14x_1 + 0.59x_2 \geq 19$
3. $-8x_1 + 3x_2 \geq 0$
4. $5.39x_1 + 7.75x_2 \leq 30$
5. $4.14x_1 + 0.59x_2 \leq 54$

## 3: Provide the symbolic representation in the required format
```json
{
    'sym_variables': [('x1', 'grams of protein'), ('x2', 'grams of carbohydrates')],
    'objective_function': '9*x1 + 4*x2',
    'constraints': [
        '5.39*x1 + 7.75*x2 >= 26',
        '4.14*x1 + 0.59*x2 >= 19',
        '-8*x1 + 3*x2 >= 0',
        '5.39*x1 + 7.75*x2 <= 30',
        '4.14*x1 + 0.59*x2 <= 54'
    ]
}
```

## 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="protein", lb=0)  # grams of protein
    x2 = model.addVar(name="carbohydrates", lb=0)  # grams of carbohydrates

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

    # Define the constraints
    model.addConstr(5.39 * x1 + 7.75 * x2 >= 26)
    model.addConstr(4.14 * x1 + 0.59 * x2 >= 19)
    model.addConstr(-8 * x1 + 3 * x2 >= 0)
    model.addConstr(5.39 * x1 + 7.75 * x2 <= 30)
    model.addConstr(4.14 * x1 + 0.59 * x2 <= 54)

    # Optimize the model
    model.optimize()

    # Print the status of the optimization
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Grams of protein: {x1.varValue}")
        print(f"Grams of carbohydrates: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    elif model.status == gurobi.GRB.Status.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model has a non-optimal status.")

optimize_problem()
```