To tackle the problem described, we first need to break down the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints.

### Symbolic Representation

Let's define:
- $x_1$ as 'milligrams of vitamin E'
- $x_2$ as 'milligrams of zinc'

The objective function to minimize is: $1.63x_1 + 8.5x_2$

Given the resources/attributes, we have constraints related to muscle growth index and immune support index for both vitamin E and zinc.

### Constraints

1. Muscle growth index from milligrams of vitamin E: $0.6x_1$
2. Immune support index from milligrams of vitamin E: $1.24x_1$
3. Muscle growth index from milligrams of zinc: $1.85x_2$
4. Immune support index from milligrams of zinc: $1.83x_2$

Combined constraints:
- Total muscle growth index $\geq 21$: $0.6x_1 + 1.85x_2 \geq 21$
- Total immune support index $\geq 21$: $1.24x_1 + 1.83x_2 \geq 21$
- Additional constraint: $7x_1 - 5x_2 \geq 0$
- Upper bound on total muscle growth index: $0.6x_1 + 1.85x_2 \leq 33$
- Upper bound on total immune support index: $1.24x_1 + 1.83x_2 \leq 26$

### Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'milligrams of vitamin E'), ('x2', 'milligrams of zinc')],
    'objective_function': '1.63*x1 + 8.5*x2',
    'constraints': [
        '0.6*x1 + 1.85*x2 >= 21',
        '1.24*x1 + 1.83*x2 >= 21',
        '7*x1 - 5*x2 >= 0',
        '0.6*x1 + 1.85*x2 <= 33',
        '1.24*x1 + 1.83*x2 <= 26'
    ]
}
```

### Gurobi Code

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(name='milligrams_of_vitamin_E', lb=-GRB.INFINITY, ub=GRB.INFINITY)
x2 = m.addVar(name='milligrams_of_zinc', lb=-GRB.INFINITY, ub=GRB.INFINITY)

# Set the objective function
m.setObjective(1.63*x1 + 8.5*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(0.6*x1 + 1.85*x2 >= 21, name='total_muscle_growth_index_lower_bound')
m.addConstr(1.24*x1 + 1.83*x2 >= 21, name='total_immune_support_index_lower_bound')
m.addConstr(7*x1 - 5*x2 >= 0, name='additional_constraint')
m.addConstr(0.6*x1 + 1.85*x2 <= 33, name='total_muscle_growth_index_upper_bound')
m.addConstr(1.24*x1 + 1.83*x2 <= 26, name='total_immune_support_index_upper_bound')

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Milligrams of Vitamin E:", x1.x)
    print("Milligrams of Zinc:", x2.x)
else:
    print("No optimal solution found.")
```