To solve the given optimization problem using Gurobi, we first need to understand and possibly simplify or clarify the constraints provided:

1. The objective function to minimize is: $5.76 \times \text{milligrams of calcium} + 4.41 \times \text{milligrams of vitamin E}$.
2. Constraints:
   - The immune support index from milligrams of calcium is 20.
   - The immune support index from milligrams of vitamin E is 13.
   - The total combined immune support index should be at least 48.
   - The expression $-9 \times \text{milligrams of calcium} + 10 \times \text{milligrams of vitamin E}$ should be no less than zero.
   - The total combined immune support index should be equal to or less than 129.

However, there seems to be a misunderstanding in the constraints as provided. For clarity and based on standard optimization problem formulation, let's interpret these requirements into mathematical expressions where:
- Let $x_0$ represent milligrams of calcium.
- Let $x_1$ represent milligrams of vitamin E.

Given:
- Objective function: $\text{Minimize} \quad 5.76x_0 + 4.41x_1$
- Constraints:
  1. $20x_0 + 13x_1 \geq 48$ (Total combined immune support index should be at least 48)
  2. $-9x_0 + 10x_1 \geq 0$ (Given constraint)
  3. $20x_0 + 13x_1 \leq 129$ (Total combined immune support index should not exceed 129)

Note: The individual immune support indices for calcium and vitamin E are given as constants (20 and 13, respectively), but they seem to be part of the constraint related to the total immune support index. Thus, we directly use these in forming our constraints without needing separate constraints for each.

Here is how you can represent this problem using Gurobi in Python:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(name="milligrams_of_calcium", lb=0)  # Lower bound set to 0, assuming non-negativity
x1 = m.addVar(name="milligrams_of_vitamin_E", lb=0)  # Lower bound set to 0, assuming non-negativity

# Set the objective function
m.setObjective(5.76*x0 + 4.41*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(20*x0 + 13*x1 >= 48, name="immune_support_index_min")
m.addConstr(-9*x0 + 10*x1 >= 0, name="expression_constraint")
m.addConstr(20*x0 + 13*x1 <= 129, name="immune_support_index_max")

# Optimize the model
m.optimize()

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