## Step 1: Define the symbolic representation of the problem
The problem involves minimizing an objective function subject to several constraints. The variables are 'knishes', 'bowls of instant ramen', and 'pickles', which we can represent symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $5x_0 + 2x_1 + 8x_2$.

## 3: List the constraints in symbolic notation
The constraints given are:
1. $4x_1 + 7x_2 \geq 32$ (at least 32 grams of fiber from bowls of instant ramen and pickles)
2. $9x_0 + 4x_1 \geq 21$ (at least 21 grams of fiber from knishes and bowls of instant ramen)
3. $9x_0 + 4x_1 + 7x_2 \geq 21$ (at least 21 grams of fiber from all sources)
4. $3x_0 + 8x_1 \geq 27$ (at least 27 milligrams of iron from knishes and bowls of instant ramen)
5. $8x_1 + 4x_2 \geq 25$ (at least 25 milligrams of iron from bowls of instant ramen and pickles)
6. $3x_0 + 4x_2 \geq 33$ (at least 33 milligrams of iron from knishes and pickles)
7. $3x_0 + 8x_1 + 4x_2 \geq 33$ (at least 33 milligrams of iron from all sources)
8. $x_0 - 10x_1 \geq 0$ (constraint on knishes and bowls of instant ramen)
9. $8x_1 - 2x_2 \geq 0$ (constraint on bowls of instant ramen and pickles)
10. $8x_1 + 4x_2 \leq 54$ (at most 54 milligrams of iron from bowls of instant ramen and pickles)

## 4: Define the symbolic variables and constraints for output
The symbolic variables are:
- $x_0$ for 'knishes'
- $x_1$ for 'bowls of instant ramen'
- $x_2$ for 'pickles'

## 5: Output the symbolic representation of the problem
```json
{
'sym_variables': [('x0', 'knishes'), ('x1', 'bowls of instant ramen'), ('x2', 'pickles')],
'objective_function': '5*x0 + 2*x1 + 8*x2',
'constraints': [
    '4*x1 + 7*x2 >= 32',
    '9*x0 + 4*x1 >= 21',
    '9*x0 + 4*x1 + 7*x2 >= 21',
    '3*x0 + 8*x1 >= 27',
    '8*x1 + 4*x2 >= 25',
    '3*x0 + 4*x2 >= 33',
    '3*x0 + 8*x1 + 4*x2 >= 33',
    'x0 - 10*x1 >= 0',
    '8*x1 - 2*x2 >= 0',
    '8*x1 + 4*x2 <= 54'
]
}
```

## 6: Implement the problem in Gurobi
```python
import gurobi

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

    # Define the variables
    knishes = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="knishes")
    bowls_of_instant_ramen = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="bowls_of_instant_ramen")
    pickles = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="pickles")

    # Define the objective function
    model.setObjective(5 * knishes + 2 * bowls_of_instant_ramen + 8 * pickles, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(4 * bowls_of_instant_ramen + 7 * pickles >= 32, name="fiber_from_ramen_and_pickles")
    model.addConstr(9 * knishes + 4 * bowls_of_instant_ramen >= 21, name="fiber_from_knishes_and_ramen")
    model.addConstr(9 * knishes + 4 * bowls_of_instant_ramen + 7 * pickles >= 21, name="total_fiber")
    model.addConstr(3 * knishes + 8 * bowls_of_instant_ramen >= 27, name="iron_from_knishes_and_ramen")
    model.addConstr(8 * bowls_of_instant_ramen + 4 * pickles >= 25, name="iron_from_ramen_and_pickles")
    model.addConstr(3 * knishes + 4 * pickles >= 33, name="iron_from_knishes_and_pickles")
    model.addConstr(3 * knishes + 8 * bowls_of_instant_ramen + 4 * pickles >= 33, name="total_iron")
    model.addConstr(knishes - 10 * bowls_of_instant_ramen >= 0, name="knishes_vs_ramen")
    model.addConstr(8 * bowls_of_instant_ramen - 2 * pickles >= 0, name="ramen_vs_pickles")
    model.addConstr(8 * bowls_of_instant_ramen + 4 * pickles <= 54, name="iron_limit")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("Knishes: ", knishes.x)
        print("Bowls of instant ramen: ", bowls_of_instant_ramen.x)
        print("Pickles: ", pickles.x)
    else:
        print("Model is infeasible")

solve_optimization_problem()
```