## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of magnesium' and 'milligrams of vitamin B4', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The objective function to maximize is $5.65x_1 + 3.36x_2$.

## Step 3: List the constraints in symbolic notation
The constraints are:
1. $9x_1$ (immune support index for $x_1$ is 9, but this seems to be an attribute rather than a constraint, similarly for $x_2$: $4x_2$),
2. $9x_1 + 4x_2 \geq 15$ (total combined immune support index from $x_1$ and $x_2$ has to be 15 or more),
3. $6x_1 - 3x_2 \geq 0$ (six times $x_1$ plus -3 times $x_2$ should be at minimum zero),
4. $9x_1 + 4x_2 \leq 29$ (total combined immune support index from $x_1$ and $x_2$ must be 29 or less, redundant with the previous similar statement).

## 4: Correctly interpret the given attributes as constraints or variable properties
- The immune support index for milligrams of magnesium is given as $x_0 = 9$ and for vitamin B4 as $x_1 = 4$, but it seems there was a mix-up in indices. Let's assume $x_1$ represents milligrams of magnesium and $x_2$ represents milligrams of vitamin B4. Thus, we have:
- $x_1$ has an immune support index of 9,
- $x_2$ has an immune support index of 4.

## 5: Formulate the optimization problem in a standard form
Maximize $5.65x_1 + 3.36x_2$,
subject to:
- $9x_1 + 4x_2 \geq 15$,
- $6x_1 - 3x_2 \geq 0$,
- $9x_1 + 4x_2 \leq 29$.

## 6: Symbolic representation
```json
{
    'sym_variables': [('x1', 'milligrams of magnesium'), ('x2', 'milligrams of vitamin B4')],
    'objective_function': '5.65*x1 + 3.36*x2',
    'constraints': [
        '9*x1 + 4*x2 >= 15',
        '6*x1 - 3*x2 >= 0',
        '9*x1 + 4*x2 <= 29'
    ]
}
```

## 7: Gurobi Python Code
```python
import gurobipy as gp

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

# Define variables
x1 = m.addVar(name="milligrams_of_magnesium", lb=0)  # Assuming non-negative
x2 = m.addVar(name="milligrams_of_vitamin_B4", lb=0, integrality=0)  # Can be fractional

# Objective function
m.setObjective(5.65*x1 + 3.36*x2, gp.GRB.MAXIMIZE)

# Constraints
m.addConstr(9*x1 + 4*x2 >= 15, name="immune_support_index_min")
m.addConstr(6*x1 - 3*x2 >= 0, name="six_magnesium_minus_three_B4")
m.addConstr(9*x1 + 4*x2 <= 29, name="immune_support_index_max")

# Solve the model
m.optimize()

# Print solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of magnesium: {x1.varValue}")
    print(f"Milligrams of vitamin B4: {x2.varValue}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```