## Step 1: Define the symbolic representation of the variables
The variables are 'bowls of pasta', 'corn cobs', and 'lemons', which can be represented symbolically 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:
- $x_0$ : 'bowls of pasta'
- $x_1$ : 'corn cobs'
- $x_2$ : 'lemons'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $9x_0 + 4x_1 + 2x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $3.46x_1 + 4.79x_2 \geq 20$ (At least 20 milligrams of calcium must come from corn cobs plus lemons)
- $5.02x_0 + 4.79x_2 \geq 23$ (At least 23 milligrams of calcium must come from bowls of pasta and lemons)
- $5.02x_0 + 3.46x_1 + 4.79x_2 \geq 51$ (At least 51 milligrams of calcium from all sources)
- $5.02x_0 + 3.46x_1 \leq 144$ (At most 144 milligrams of calcium from bowls of pasta and corn cobs)
- $3.46x_1 + 4.79x_2 \leq 83$ (At most 83 milligrams of calcium from corn cobs and lemons)
- $5.02x_0 + 3.46x_1 + 4.79x_2 \leq 83$ (At most 83 milligrams of calcium from all sources, redundant with previous)
- $1.76x_0 + 2.08x_1 \leq 42$ (Healthiness rating from bowls of pasta and corn cobs)
- $2.08x_1 + 1.73x_2 \leq 41$ (Healthiness rating from corn cobs and lemons)
- $1.76x_0 + 2.08x_1 + 1.73x_2 \leq 46$ (Total healthiness rating)

## 5: Correcting and Finalizing the Symbolic Representation
Given that $x_0$, $x_1$, and $x_2$ represent 'bowls of pasta', 'corn cobs', and 'lemons' respectively, the correct symbolic representation is:
```json
{
'sym_variables': [('x0', 'bowls of pasta'), ('x1', 'corn cobs'), ('x2', 'lemons')],
'objective_function': '9*x0 + 4*x1 + 2*x2',
'constraints': [
    '3.46*x1 + 4.79*x2 >= 20',
    '5.02*x0 + 4.79*x2 >= 23',
    '5.02*x0 + 3.46*x1 + 4.79*x2 >= 51',
    '5.02*x0 + 3.46*x1 <= 144',
    '3.46*x1 + 4.79*x2 <= 83',
    '5.02*x0 + 3.46*x1 + 4.79*x2 <= 83',
    '1.76*x0 + 2.08*x1 <= 42',
    '2.08*x1 + 1.73*x2 <= 41',
    '1.76*x0 + 2.08*x1 + 1.73*x2 <= 46'
]
}
```

## 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="bowls_of_pasta", lb=0)  # bowls of pasta
    x1 = model.addVar(name="corn_cobs", lb=0)    # corn cobs
    x2 = model.addVar(name="lemons", lb=0)      # lemons

    # Objective function
    model.setObjective(9 * x0 + 4 * x1 + 2 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(3.46 * x1 + 4.79 * x2 >= 20)  # At least 20 milligrams of calcium from corn cobs and lemons
    model.addConstr(5.02 * x0 + 4.79 * x2 >= 23)  # At least 23 milligrams of calcium from bowls of pasta and lemons
    model.addConstr(5.02 * x0 + 3.46 * x1 + 4.79 * x2 >= 51)  # At least 51 milligrams of calcium from all
    model.addConstr(5.02 * x0 + 3.46 * x1 <= 144)  # At most 144 milligrams of calcium from bowls of pasta and corn cobs
    model.addConstr(3.46 * x1 + 4.79 * x2 <= 83)  # At most 83 milligrams of calcium from corn cobs and lemons
    # model.addConstr(5.02 * x0 + 3.46 * x1 + 4.79 * x2 <= 83)  # This constraint is redundant
    model.addConstr(1.76 * x0 + 2.08 * x1 <= 42)  # Healthiness rating from bowls of pasta and corn cobs
    model.addConstr(2.08 * x1 + 1.73 * x2 <= 41)  # Healthiness rating from corn cobs and lemons
    model.addConstr(1.76 * x0 + 2.08 * x1 + 1.73 * x2 <= 46)  # Total healthiness rating

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("bowls of pasta: ", x0.varValue)
        print("corn cobs: ", x1.varValue)
        print("lemons: ", x2.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```