## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are 'oreos', 'bagged salads', and 'peanutbutter sandwiches', which we can denote as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $2.58x_1 + 8.22x_2 + 5.51x_3$.

## 3: Define the constraints in symbolic notation
The constraints given are:
- $11x_1 + 6x_2 + 3x_3 \leq 186$ (total iron constraint, but we need to consider the specific constraints below)
- $6x_2 + 3x_3 \leq 180$ (iron from bagged salads and peanutbutter sandwiches)
- $11x_1 + 3x_3 \leq 75$ (iron from oreos and peanutbutter sandwiches)
- $11x_1 + 6x_2 \leq 90$ (iron from oreos and bagged salads)
- $11x_1 + 6x_2 + 3x_3 \leq 90$ (iron from all, but this seems to be a repetition or error given the total possible is 186)
- $x_1$ is an integer (oreos)
- $x_2$ is an integer (bagged salads)
- $x_3$ can be any real number (peanutbutter sandwiches)

## 4: Correct and clarify constraints
Upon review, it seems there might have been a misunderstanding in directly translating constraints. The actual constraints to consider based on the problem statement are:
- $6x_2 + 3x_3 \leq 180$
- $11x_1 + 3x_3 \leq 75$
- $11x_1 + 6x_2 \leq 90$
- $11x_1 + 6x_2 + 3x_3 \leq 186$

## 5: Symbolic representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'oreos'), ('x2', 'bagged salads'), ('x3', 'peanutbutter sandwiches')],
'objective_function': '2.58*x1 + 8.22*x2 + 5.51*x3',
'constraints': [
    '6*x2 + 3*x3 <= 180',
    '11*x1 + 3*x3 <= 75',
    '11*x1 + 6*x2 <= 90',
    '11*x1 + 6*x2 + 3*x3 <= 186',
    'x1 >= 0 and x1 is an integer',
    'x2 >= 0 and x2 is an integer',
    'x3 >= 0'
]
}
```

## 6: Gurobi Code
Now, let's write the Gurobi code to solve this problem:
```python
import gurobi

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

    # Define variables
    oreos = model.addVar(name="oreos", vtype=gurobi.GRB.INTEGER)
    bagged_salads = model.addVar(name="bagged_salads", vtype=gurobi.GRB.INTEGER)
    peanutbutter_sandwiches = model.addVar(name="peanutbutter_sandwiches")

    # Objective function
    model.setObjective(2.58 * oreos + 8.22 * bagged_salads + 5.51 * peanutbutter_sandwiches, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(6 * bagged_salads + 3 * peanutbutter_sandwiches <= 180)
    model.addConstr(11 * oreos + 3 * peanutbutter_sandwiches <= 75)
    model.addConstr(11 * oreos + 6 * bagged_salads <= 90)
    model.addConstr(11 * oreos + 6 * bagged_salads + 3 * peanutbutter_sandwiches <= 186)

    # Non-negativity constraints
    oreos.lb = 0
    bagged_salads.lb = 0
    peanutbutter_sandwiches.lb = 0

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Oreos: ", oreos.x)
        print("Bagged Salads: ", bagged_salads.x)
        print("Peanutbutter Sandwiches: ", peanutbutter_sandwiches.x)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```