## Step 1: Define the symbolic representation of the variables
The variables are 'bagged salads', 'kiwis', and 'strips of bacon', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ for 'bagged salads'
- $x_2$ for 'kiwis'
- $x_3$ for 'strips of bacon'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $5.76x_1^2 + 7.22x_2x_3 + 7.84x_3^2 + 2.36x_3$.

## 4: Define the constraints in symbolic notation
The constraints are:
- $20x_1 + 18x_2 + 7x_3 \leq 300$ (total iron constraint)
- $18^2x_2^2 + 7^2x_3^2 \geq 62$ (iron from kiwis squared plus strips of bacon squared constraint)
- $20x_1 + 7x_3 \geq 99$ (iron from bagged salads and strips of bacon constraint)
- $18^2x_2^2 + 7^2x_3^2 \leq 221$ (upper bound on iron from kiwis squared and strips of bacon squared)
- $20^2x_1^2 + 7^2x_3^2 \leq 203$ (upper bound on iron from bagged salads squared and strips of bacon squared)
- $20x_1 + 18x_2 + 7x_3 \leq 203$ (upper bound on total iron from all sources)

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'bagged salads'), ('x2', 'kiwis'), ('x3', 'strips of bacon')],
    'objective_function': '5.76*x1^2 + 7.22*x2*x3 + 7.84*x3^2 + 2.36*x3',
    'constraints': [
        '20*x1 + 18*x2 + 7*x3 <= 300',
        '18^2*x2^2 + 7^2*x3^2 >= 62',
        '20*x1 + 7*x3 >= 99',
        '18^2*x2^2 + 7^2*x3^2 <= 221',
        '20^2*x1^2 + 7^2*x3^2 <= 203',
        '20*x1 + 18*x2 + 7*x3 <= 203'
    ]
}
```

## Step 6: Implement the optimization problem using Gurobi
```python
import gurobi

# Create a new model
m = gurobi.Model()

# Define the variables
x1 = m.addVar(name="bagged_salads", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x2 = m.addVar(name="kiwis", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x3 = m.addVar(name="strips_of_bacon", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

# Objective function
m.setObjective(5.76 * x1**2 + 7.22 * x2 * x3 + 7.84 * x3**2 + 2.36 * x3, gurobi.GRB.MAXIMIZE)

# Constraints
m.addConstr(20 * x1 + 18 * x2 + 7 * x3 <= 300)
m.addConstr(18**2 * x2**2 + 7**2 * x3**2 >= 62)
m.addConstr(20 * x1 + 7 * x3 >= 99)
m.addConstr(18**2 * x2**2 + 7**2 * x3**2 <= 221)
m.addConstr(20**2 * x1**2 + 7**2 * x3**2 <= 203)
m.addConstr(20 * x1 + 18 * x2 + 7 * x3 <= 203)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.Status.OPTIMAL:
    print("Objective: ", m.objVal)
    print("bagged salads: ", x1.varValue)
    print("kiwis: ", x2.varValue)
    print("strips of bacon: ", x3.varValue)
else:
    print("The model is infeasible")
```