To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables for the quantities of interest (grams of protein and grams of carbohydrates), formulating the objective function using these variables, and then expressing the constraints in terms of these variables.

Let's denote:
- \(x_0\) as the quantity of grams of protein.
- \(x_1\) as the quantity of grams of carbohydrates.

The objective function to minimize is given by \(9x_0 + 4x_1\).

Now, let's express the constraints symbolically:

1. The total combined immune support index from grams of protein and grams of carbohydrates must be greater than or equal to 26: \(5.39x_0 + 7.75x_1 \geq 26\).
2. This constraint is already included in the previous one, so we ignore it as a separate entity.
3. The total combined cardiovascular support index from grams of protein plus grams of carbohydrates should be no less than 19: \(4.14x_0 + 0.59x_1 \geq 19\).
4. This constraint is the same as the previous one, so we ignore it for duplication.
5. \(-8x_0 + 3x_1 \geq 0\).
6. The total combined immune support index from grams of protein plus grams of carbohydrates has to be less than or equal to 30: \(5.39x_0 + 7.75x_1 \leq 30\).
7. The total combined cardiovascular support index from grams of protein plus grams of carbohydrates should be 54 at a maximum: \(4.14x_0 + 0.59x_1 \leq 54\).

Given the problem's requirements, we can represent it symbolically as follows:

```json
{
    'sym_variables': [('x0', 'grams of protein'), ('x1', 'grams of carbohydrates')],
    'objective_function': '9*x0 + 4*x1',
    'constraints': [
        '5.39*x0 + 7.75*x1 >= 26',
        '4.14*x0 + 0.59*x1 >= 19',
        '-8*x0 + 3*x1 >= 0',
        '5.39*x0 + 7.75*x1 <= 30',
        '4.14*x0 + 0.59*x1 <= 54'
    ]
}
```

To solve this optimization problem using Gurobi, we'll write the Python code as follows:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(lb=0, name="grams_of_protein")
x1 = m.addVar(lb=0, name="grams_of_carbohydrates")

# Set the objective function
m.setObjective(9*x0 + 4*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(5.39*x0 + 7.75*x1 >= 26, name="immune_support_min")
m.addConstr(4.14*x0 + 0.59*x1 >= 19, name="cardiovascular_support_min")
m.addConstr(-8*x0 + 3*x1 >= 0, name="protein_carb_ratio")
m.addConstr(5.39*x0 + 7.75*x1 <= 30, name="immune_support_max")
m.addConstr(4.14*x0 + 0.59*x1 <= 54, name="cardiovascular_support_max")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Grams of protein: {x0.x}")
    print(f"Grams of carbohydrates: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```