## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin D', 'grams of fiber', and 'milligrams of vitamin B5'. Let's denote them as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : milligrams of vitamin D
- $x_1$ : grams of fiber
- $x_2$ : milligrams of vitamin B5

## 3: Define the objective function in symbolic notation
The objective function to maximize is $1.99x_0 + 4.21x_1 + 6.01x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $9x_0 \leq 82$
- $7x_0 \leq 90$
- $8x_1 \leq 82$
- $1x_1 \leq 90$
- $10x_2 \leq 82$
- $10x_2 \leq 90$
- $8x_1 + 10x_2 \leq 58$
- $9x_0 + 8x_1 \leq 33$
- $9x_0 + 8x_1 + 10x_2 \leq 33$
- $1x_1 + 10x_2 \leq 37$
- $7x_0 + 1x_1 \leq 38$
- $7x_0 + 10x_2 \leq 31$
- $7x_0 + 1x_1 + 10x_2 \leq 31$

## 5: Correcting and Simplifying Constraints
Upon review, some constraints seem to be incorrectly transcribed from the problem statement, particularly those involving upper bounds of resources $r0$ and $r1$. Let's correct and simplify:
- $9x_0 \leq 82$
- $7x_0 \leq 90$
- $8x_1 \leq 82$
- $x_1 \leq 90$
- $10x_2 \leq 82$
- $10x_2 \leq 90$
- $8x_1 + 10x_2 \leq 58$
- $9x_0 + 8x_1 \leq 33$
- $9x_0 + 8x_1 + 10x_2 \leq 33$
- $x_1 + 10x_2 \leq 37$
- $7x_0 + x_1 \leq 38$
- $7x_0 + 10x_2 \leq 31$
- $7x_0 + x_1 + 10x_2 \leq 31$

## 6: Writing the Gurobi Code
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="milligrams_of_vitamin_D", lb=0)
    x1 = model.addVar(name="grams_of_fiber", lb=0)
    x2 = model.addVar(name="milligrams_of_vitamin_B5", lb=0)

    # Define the objective function
    model.setObjective(1.99*x0 + 4.21*x1 + 6.01*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(9*x0 <= 82)
    model.addConstr(7*x0 <= 90)
    model.addConstr(8*x1 <= 82)
    model.addConstr(x1 <= 90)
    model.addConstr(10*x2 <= 82)
    model.addConstr(10*x2 <= 90)
    model.addConstr(8*x1 + 10*x2 <= 58)
    model.addConstr(9*x0 + 8*x1 <= 33)
    model.addConstr(9*x0 + 8*x1 + 10*x2 <= 33)
    model.addConstr(x1 + 10*x2 <= 37)
    model.addConstr(7*x0 + x1 <= 38)
    model.addConstr(7*x0 + 10*x2 <= 31)
    model.addConstr(7*x0 + x1 + 10*x2 <= 31)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin D: {x0.varValue}")
        print(f"Grams of fiber: {x1.varValue}")
        print(f"Milligrams of vitamin B5: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

## 7: Symbolic Representation
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin D'),
        ('x1', 'grams of fiber'),
        ('x2', 'milligrams of vitamin B5')
    ],
    'objective_function': '1.99*x0 + 4.21*x1 + 6.01*x2',
    'constraints': [
        '9*x0 <= 82',
        '7*x0 <= 90',
        '8*x1 <= 82',
        'x1 <= 90',
        '10*x2 <= 82',
        '10*x2 <= 90',
        '8*x1 + 10*x2 <= 58',
        '9*x0 + 8*x1 <= 33',
        '9*x0 + 8*x1 + 10*x2 <= 33',
        'x1 + 10*x2 <= 37',
        '7*x0 + x1 <= 38',
        '7*x0 + 10*x2 <= 31',
        '7*x0 + x1 + 10*x2 <= 31'
    ]
}
```