## Step 1: Define the symbolic representation of the problem
The problem involves variables 'oreos', 'lemons', and 'pickles', which can be represented 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 $6 \times \text{oreos} + 8 \times \text{lemons} + 3 \times \text{pickles}$, which in symbolic notation is $6x_0 + 8x_1 + 3x_2$.

## 3: Define the resources/attributes in symbolic notation
Given resources are:
- $r_0$: milligrams of calcium with $x_0 = 7$, $x_1 = 1$, $x_2 = 6$ and an upper bound of 43.
- $r_1$: healthiness rating with $x_0 = 2$, $x_1 = 8$, $x_2 = 1$ and an upper bound of 34.

## 4: Translate the constraints into symbolic notation
Constraints are:
1. $x_1 + x_2 \geq 10$ (at least 10 milligrams of calcium from lemons and pickles)
2. $7x_0 + x_1 \geq 4$ (at least 4 milligrams of calcium from oreos and lemons)
3. $7x_0 + x_1 + 6x_2 \geq 4$ (at least 4 milligrams of calcium from all)
4. $8x_1 + x_2 \geq 7$ (healthiness rating from lemons and pickles)
5. $2x_0 + 8x_1 \geq 9$ (healthiness rating from oreos and lemons)
6. $2x_0 + 8x_1 + x_2 \geq 9$ (healthiness rating from all)
7. $4x_1 - 7x_2 \geq 0$
8. $7x_0 + x_1 \leq 22$ (at most 22 milligrams of calcium from oreos and lemons)
9. $x_1 + 6x_2 \leq 29$ (up to 29 milligrams of calcium from lemons and pickles)
10. $8x_1 + x_2 \leq 25$ (healthiness rating limit from lemons and pickles)

## 5: Symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'oreos'), ('x1', 'lemons'), ('x2', 'pickles')],
    'objective_function': '6*x0 + 8*x1 + 3*x2',
    'constraints': [
        'x1 + x2 >= 10',
        '7*x0 + x1 >= 4',
        '7*x0 + x1 + 6*x2 >= 4',
        '8*x1 + x2 >= 7',
        '2*x0 + 8*x1 >= 9',
        '2*x0 + 8*x1 + x2 >= 9',
        '4*x1 - 7*x2 >= 0',
        '7*x0 + x1 <= 22',
        'x1 + 6*x2 <= 29',
        '8*x1 + x2 <= 25'
    ]
}
```

## 6: Gurobi code for the optimization problem
```python
import gurobi

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

    # Define variables
    oreos = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="oreos")
    lemons = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="lemons")
    pickles = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="pickles")

    # Objective function
    model.setObjective(6 * oreos + 8 * lemons + 3 * pickles, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(lemons + pickles >= 10, name="calcium_from_lemons_pickles")
    model.addConstr(7 * oreos + lemons >= 4, name="calcium_from_oreos_lemons")
    model.addConstr(7 * oreos + lemons + 6 * pickles >= 4, name="calcium_from_all")
    model.addConstr(8 * lemons + pickles >= 7, name="healthiness_from_lemons_pickles")
    model.addConstr(2 * oreos + 8 * lemons >= 9, name="healthiness_from_oreos_lemons")
    model.addConstr(2 * oreos + 8 * lemons + pickles >= 9, name="healthiness_from_all")
    model.addConstr(4 * lemons - 7 * pickles >= 0, name="lemons_pickles_relation")
    model.addConstr(7 * oreos + lemons <= 22, name="calcium_limit_oreos_lemons")
    model.addConstr(lemons + 6 * pickles <= 29, name="calcium_limit_lemons_pickles")
    model.addConstr(8 * lemons + pickles <= 25, name="healthiness_limit_lemons_pickles")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Oreos: ", oreos.x)
        print("Lemons: ", lemons.x)
        print("Pickles: ", pickles.x)
    else:
        print("No optimal solution found")

optimize_problem()
```