To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. The variables are 'milligrams of calcium' and 'milligrams of vitamin E', which we will denote as `x1` and `x2`, respectively.

The objective function is to minimize `5.76*x1 + 4.41*x2`.

The constraints given are:
- The immune support index of milligrams of calcium is 20, so `20*x1`.
- The immune support index of milligrams of vitamin E is 13, so `13*x2`.
- The total combined immune support index from both should be at least 48: `20*x1 + 13*x2 >= 48`.
- Another constraint is `-9*x1 + 10*x2 >= 0`.
- The total combined immune support index should not exceed 231, but a specific upper limit of 129 for the combination is mentioned instead: `20*x1 + 13*x2 <= 129`.

Given these details, we can represent the problem symbolically as follows:

```json
{
  'sym_variables': [('x1', 'milligrams of calcium'), ('x2', 'milligrams of vitamin E')],
  'objective_function': 'minimize 5.76*x1 + 4.41*x2',
  'constraints': [
    '20*x1 + 13*x2 >= 48',
    '-9*x1 + 10*x2 >= 0',
    '20*x1 + 13*x2 <= 129'
  ]
}
```

Now, to solve this optimization problem using Gurobi, we can write the following Python code:

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(lb=0, name="milligrams_of_calcium")
x2 = m.addVar(lb=0, name="milligrams_of_vitamin_E")

# Set the objective function
m.setObjective(5.76*x1 + 4.41*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(20*x1 + 13*x2 >= 48, "Immune_Support_Index_Min")
m.addConstr(-9*x1 + 10*x2 >= 0, "Calcium_Vitamin_E_Balance")
m.addConstr(20*x1 + 13*x2 <= 129, "Total_Immune_Support_Max")

# Optimize the model
m.optimize()

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