## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B7', 'milligrams of calcium', 'milligrams of vitamin B2', 'milligrams of vitamin E']. Let's denote them as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The pairs of variables in symbolic notation are: 
- $x_1$ for 'milligrams of vitamin B7'
- $x_2$ for 'milligrams of calcium'
- $x_3$ for 'milligrams of vitamin B2'
- $x_4$ for 'milligrams of vitamin E'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $9x_1 + 6x_2 + 9x_3 + 6x_4$.

## 4: List the constraints in symbolic notation
The constraints are:
- $8x_1 \leq 85$
- $3x_2 \leq 85$
- $4x_3 \leq 85$
- $4x_4 \leq 85$
- $8x_1 + 4x_4 \geq 14$
- $3x_2 + 4x_3 + 4x_4 \geq 12$
- $3x_2 + 4x_3 \leq 22$
- $8x_1 + 4x_4 \leq 72$
- $8x_1 + 4x_3 \leq 80$
- $8x_1 + 3x_2 + 4x_3 + 4x_4 \leq 85$
- $8x_1 + 3x_2 + 4x_4 \leq 36$
- $3x_2 + 4x_3 + 4x_4 \leq 45$
- $8x_1 + 3x_2 + 4x_3 + 4x_4 \leq 45$

## 5: Consider the variable bounds
All $x_i$ can be non-integer.

## 6: Write down the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B7'), 
        ('x2', 'milligrams of calcium'), 
        ('x3', 'milligrams of vitamin B2'), 
        ('x4', 'milligrams of vitamin E')
    ], 
    'objective_function': '9*x1 + 6*x2 + 9*x3 + 6*x4', 
    'constraints': [
        '8*x1 <= 85',
        '3*x2 <= 85',
        '4*x3 <= 85',
        '4*x4 <= 85',
        '8*x1 + 4*x4 >= 14',
        '3*x2 + 4*x3 + 4*x4 >= 12',
        '3*x2 + 4*x3 <= 22',
        '8*x1 + 4*x4 <= 72',
        '8*x1 + 4*x3 <= 80',
        '8*x1 + 3*x2 + 4*x3 + 4*x4 <= 85',
        '8*x1 + 3*x2 + 4*x4 <= 36',
        '3*x2 + 4*x3 + 4*x4 <= 45',
        '8*x1 + 3*x2 + 4*x3 + 4*x4 <= 45'
    ]
}
```

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

def optimize_problem():
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="x1", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B7
    x2 = model.addVar(name="x2", lb=-gurobi.GRB.INFINITY)  # milligrams of calcium
    x3 = model.addVar(name="x3", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B2
    x4 = model.addVar(name="x4", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin E

    # Objective function
    model.setObjective(9 * x1 + 6 * x2 + 9 * x3 + 6 * x4, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(8 * x1 <= 85)
    model.addConstr(3 * x2 <= 85)
    model.addConstr(4 * x3 <= 85)
    model.addConstr(4 * x4 <= 85)
    model.addConstr(8 * x1 + 4 * x4 >= 14)
    model.addConstr(3 * x2 + 4 * x3 + 4 * x4 >= 12)
    model.addConstr(3 * x2 + 4 * x3 <= 22)
    model.addConstr(8 * x1 + 4 * x4 <= 72)
    model.addConstr(8 * x1 + 4 * x3 <= 80)
    model.addConstr(8 * x1 + 3 * x2 + 4 * x3 + 4 * x4 <= 85)
    model.addConstr(8 * x1 + 3 * x2 + 4 * x4 <= 36)
    model.addConstr(3 * x2 + 4 * x3 + 4 * x4 <= 45)
    model.addConstr(8 * x1 + 3 * x2 + 4 * x3 + 4 * x4 <= 45)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("x1: ", x1.x)
        print("x2: ", x2.x)
        print("x3: ", x3.x)
        print("x4: ", x4.x)
    else:
        print("The model is infeasible")

optimize_problem()
```