## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. Let's define the symbolic variables:
- $x_1$ represents the milligrams of vitamin E
- $x_2$ represents the milligrams of vitamin D
- $x_3$ represents the milligrams of vitamin B6

The objective function to maximize is $8x_1 + x_2 + 8x_3$.

## Step 2: Translate the constraints into symbolic notation
The constraints given are:
- The immune support index of milligrams of vitamin E is 1.79: $1.79x_1$
- Milligrams of vitamin D each have an immune support index of 1.9: $1.9x_2$
- The immune support index for milligrams of vitamin B6 is 0.03: $0.03x_3$
- The total combined immune support index from milligrams of vitamin D plus milligrams of vitamin B6 should be 65 at maximum: $1.9x_2 + 0.03x_3 \leq 65$
- The total combined immune support index from milligrams of vitamin E plus milligrams of vitamin D has to be 77 at maximum: $1.79x_1 + 1.9x_2 \leq 77$
- The total combined immune support index from milligrams of vitamin E, milligrams of vitamin D, and milligrams of vitamin B6 should be 48 or less: $1.79x_1 + 1.9x_2 + 0.03x_3 \leq 48$
- The number of milligrams of vitamin E can be a decimal: $x_1 \in \mathbb{R}$
- You can use a fractional amount of milligrams of vitamin D: $x_2 \in \mathbb{R}$
- There may be a non-integer number of milligrams of vitamin B6: $x_3 \in \mathbb{R}$

## 3: Provide the symbolic representation in the required format
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin E'), ('x2', 'milligrams of vitamin D'), ('x3', 'milligrams of vitamin B6')],
    'objective_function': '8*x1 + x2 + 8*x3',
    'constraints': [
        '1.9*x2 + 0.03*x3 <= 65',
        '1.79*x1 + 1.9*x2 <= 77',
        '1.79*x1 + 1.9*x2 + 0.03*x3 <= 48',
        'x1 >= 0', 'x2 >= 0', 'x3 >= 0'
    ]
}
```

## 4: Implement the optimization problem using Gurobi
```python
import gurobi

def optimize_vitamins():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="x1", lb=0, ub=None)  # milligrams of vitamin E
    x2 = model.addVar(name="x2", lb=0, ub=None)  # milligrams of vitamin D
    x3 = model.addVar(name="x3", lb=0, ub=None)  # milligrams of vitamin B6

    # Define the objective function
    model.setObjective(8*x1 + x2 + 8*x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(1.9*x2 + 0.03*x3 <= 65)
    model.addConstr(1.79*x1 + 1.9*x2 <= 77)
    model.addConstr(1.79*x1 + 1.9*x2 + 0.03*x3 <= 48)

    # Optimize the model
    model.optimize()

    # Print the status of the optimization
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin E: {x1.varValue}")
        print(f"Milligrams of vitamin D: {x2.varValue}")
        print(f"Milligrams of vitamin B6: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_vitamins()
```