## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B6' and 'milligrams of vitamin B12', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ for 'milligrams of vitamin B6'
- $x_2$ for 'milligrams of vitamin B12'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $9.01x_1 + 5.56x_2$.

## 4: Define the constraints in symbolic notation
The constraints are:
- $22x_1$ (immune support index of $x_1$)
- $14x_2$ (immune support index of $x_2$)
- $22x_1 + 14x_2 \geq 51$ (total combined immune support index minimum)
- $10x_1 - 10x_2 \geq 0$ (constraint on $x_1$ and $x_2$)
- $22x_1 + 14x_2 \leq 85$ (total combined immune support index maximum)

## 5: Simplify the constraint $10x_1 - 10x_2 \geq 0$
This constraint can be simplified to $x_1 \geq x_2$.

## 6: Define the semi-algebraic constraints
The constraints are:
- $22x_1 + 14x_2 \geq 51$
- $x_1 - x_2 \geq 0$
- $22x_1 + 14x_2 \leq 85$

## 7: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B6'), ('x2', 'milligrams of vitamin B12')],
    'objective_function': '9.01*x1 + 5.56*x2',
    'constraints': [
        '22*x1 + 14*x2 >= 51',
        'x1 - x2 >= 0',
        '22*x1 + 14*x2 <= 85'
    ]
}
```

## 8: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='milligrams of vitamin B6', lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name='milligrams of vitamin B12', lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

    # Define the objective function
    model.setObjective(9.01 * x1 + 5.56 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(22 * x1 + 14 * x2 >= 51, name='immune_support_min')
    model.addConstr(x1 - x2 >= 0, name='vitamin_constraint')
    model.addConstr(22 * x1 + 14 * x2 <= 85, name='immune_support_max')

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'Milligrams of vitamin B6: {x1.varValue}')
        print(f'Milligrams of vitamin B12: {x2.varValue}')
        print(f'Objective function value: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_optimization_problem()
```