To solve the optimization problem described, we first need to convert the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints using mathematical notation.

Let's define:
- $x_1$ as the milligrams of magnesium,
- $x_2$ as the milligrams of vitamin B4.

The objective function described is to maximize $5.65x_1 + 3.36x_2$.

Constraints are as follows:
1. The immune support index for milligrams of magnesium is 9: This translates to $x_1 \geq 9/9 = 1$, but since the description does not directly relate this constraint to a specific lower bound on magnesium, we will focus on constraints that explicitly limit our variables.
2. The immune support index for milligrams of vitamin B4 is 4: Similar to the first point, this implies $x_2 \geq 4/4 = 1$, but again, it seems more about a characteristic than a direct constraint.
3. The total combined immune support index from milligrams of magnesium plus milligrams of vitamin B4 has to be 15 or more: This gives us $9x_1 + 4x_2 \geq 15$.
4. Six times the number of milligrams of magnesium, plus -3 times the number of milligrams of vitamin B4 should be at minimum zero: $6x_1 - 3x_2 \geq 0$.
5. The total combined immune support index from milligrams of magnesium plus milligrams of vitamin B4 must be 29 or less: $9x_1 + 4x_2 \leq 48$, based on the provided attribute, but it seems there was a mix-up in interpreting "must be 29 or less" which might have been intended to reflect an upper limit directly associated with the 'r0' resource. However, we will use $9x_1 + 4x_2 \leq 48$ as per the given attribute.
6. The possibility of non-whole numbers for both milligrams of magnesium and vitamin B4 indicates that both $x_1$ and $x_2$ can be continuous variables.

Given this, our symbolic representation is:
```json
{
    'sym_variables': [('x1', 'milligrams of magnesium'), ('x2', 'milligrams of vitamin B4')],
    'objective_function': 'maximize 5.65*x1 + 3.36*x2',
    'constraints': [
        '9*x1 + 4*x2 >= 15',
        '6*x1 - 3*x2 >= 0',
        '9*x1 + 4*x2 <= 48'
    ]
}
```

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

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

# Add variables
x1 = model.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_magnesium")
x2 = model.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B4")

# Set the objective function
model.setObjective(5.65*x1 + 3.36*x2, GRB.MAXIMIZE)

# Add constraints
model.addConstr(9*x1 + 4*x2 >= 15, "immune_support_lower_bound")
model.addConstr(6*x1 - 3*x2 >= 0, "magnesium_vitamin_B4_ratio")
model.addConstr(9*x1 + 4*x2 <= 48, "total_immune_support_upper_bound")

# Optimize the model
model.optimize()

# Print the results
if model.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Magnesium: {x1.x}")
    print(f"Milligrams of Vitamin B4: {x2.x}")
    print(f"Objective Function Value: {model.objVal}")
else:
    print("No optimal solution found")
```