To solve the given optimization problem using Gurobi, we first need to understand and translate the natural language description into a mathematical formulation. The objective is to minimize the function \(1.63x_0 + 8.5x_1\), where \(x_0\) represents the milligrams of vitamin E and \(x_1\) represents the milligrams of zinc.

The constraints provided can be translated as follows:
1. Muscle growth index for vitamin E: \(0.6x_0\)
2. Immune support index for vitamin E: \(1.24x_0\)
3. Muscle growth index for zinc: \(1.85x_1\)
4. Immune support index for zinc: \(1.83x_1\)

The total combined muscle growth index must be at least 21 and at most 33:
\[0.6x_0 + 1.85x_1 \geq 21\]
\[0.6x_0 + 1.85x_1 \leq 33\]

The total combined immune support index must be at least 21 and at most 26:
\[1.24x_0 + 1.83x_1 \geq 21\]
\[1.24x_0 + 1.83x_1 \leq 26\]

Additionally, there's a constraint involving \(7x_0 - 5x_1 \geq 0\).

Given that both \(x_0\) (milligrams of vitamin E) and \(x_1\) (milligrams of zinc) can be fractional (non-integer), we do not need to specify them as integers in the Gurobi model.

Here's how you could implement this problem in Python using Gurobi:

```python
from gurobipy import *

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

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

# Objective function: Minimize 1.63*x0 + 8.5*x1
m.setObjective(1.63*x0 + 8.5*x1, GRB.MINIMIZE)

# Constraints
m.addConstr(0.6*x0 + 1.85*x1 >= 21, name="muscle_growth_index_min")
m.addConstr(0.6*x0 + 1.85*x1 <= 33, name="muscle_growth_index_max")
m.addConstr(1.24*x0 + 1.83*x1 >= 21, name="immune_support_index_min")
m.addConstr(1.24*x0 + 1.83*x1 <= 26, name="immune_support_index_max")
m.addConstr(7*x0 - 5*x1 >= 0, name="additional_constraint")

# Optimize model
m.optimize()

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