## Step 1: Define the symbolic representation of the variables
The variables are ['diapers', 'toilet paper rolls', 'candles', 'rubber gloves', 'dish soap bottles'] which can be represented symbolically as ['x1', 'x2', 'x3', 'x4', 'x5'].

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables and their corresponding natural language objects is:
- x1: diapers
- x2: toilet paper rolls
- x3: candles
- x4: rubber gloves
- x5: dish soap bottles

## Step 3: Define the objective function in symbolic notation
The objective function to maximize is: 2*x1 + 5*x2 + 8*x3 + 6*x4 + 5*x5

## 4: List the constraints in symbolic notation
The constraints are:
- 3*x1 <= 216
- 2*x2 <= 216
- 11*x3 <= 216
- 11*x4 <= 216
- 12*x5 <= 216
- 2*x2 + 11*x3 >= 26
- 3*x1 + 11*x4 >= 43
- 11*x3 + 11*x4 >= 16
- 11*x3 + 12*x5 >= 39
- 2*x2 + 12*x5 >= 31
- 2*x2 + 11*x4 >= 40
- 3*x1 + 2*x2 >= 20
- 2*x2 + 11*x3 <= 121
- 3*x1 + 2*x2 <= 100
- 3*x1 + 11*x3 <= 62
- 3*x1 + 2*x2 + 11*x4 <= 88
- 3*x1 + 11*x4 + 12*x5 <= 179
- 3*x1 + 2*x2 + 12*x5 <= 69
- 3*x1 + 2*x2 + 11*x3 <= 152
- 2*x2 + 11*x3 + 12*x5 <= 150
- 3*x1 + 2*x2 + 11*x3 + 11*x4 + 12*x5 <= 150
- x1, x2, x3, x4, x5 are non-negative integers

## 5: Write the Gurobi code
```python
import gurobi

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

# Define the variables
x1 = m.addVar(name='diapers', vtype=gurobi.GRB.INTEGER)
x2 = m.addVar(name='toilet paper rolls', vtype=gurobi.GRB.INTEGER)
x3 = m.addVar(name='candles', vtype=gurobi.GRB.INTEGER)
x4 = m.addVar(name='rubber gloves', vtype=gurobi.GRB.INTEGER)
x5 = m.addVar(name='dish soap bottles', vtype=gurobi.GRB.INTEGER)

# Define the objective function
m.setObjective(2*x1 + 5*x2 + 8*x3 + 6*x4 + 5*x5, gurobi.GRB.MAXIMIZE)

# Add constraints
m.addConstr(3*x1 <= 216)
m.addConstr(2*x2 <= 216)
m.addConstr(11*x3 <= 216)
m.addConstr(11*x4 <= 216)
m.addConstr(12*x5 <= 216)
m.addConstr(2*x2 + 11*x3 >= 26)
m.addConstr(3*x1 + 11*x4 >= 43)
m.addConstr(11*x3 + 11*x4 >= 16)
m.addConstr(11*x3 + 12*x5 >= 39)
m.addConstr(2*x2 + 12*x5 >= 31)
m.addConstr(2*x2 + 11*x4 >= 40)
m.addConstr(3*x1 + 2*x2 >= 20)
m.addConstr(2*x2 + 11*x3 <= 121)
m.addConstr(3*x1 + 2*x2 <= 100)
m.addConstr(3*x1 + 11*x3 <= 62)
m.addConstr(3*x1 + 2*x2 + 11*x4 <= 88)
m.addConstr(3*x1 + 11*x4 + 12*x5 <= 179)
m.addConstr(3*x1 + 2*x2 + 12*x5 <= 69)
m.addConstr(3*x1 + 2*x2 + 11*x3 <= 152)
m.addConstr(2*x2 + 11*x3 + 12*x5 <= 150)
m.addConstr(3*x1 + 2*x2 + 11*x3 + 11*x4 + 12*x5 <= 150)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', m.objVal)
    print('Diapers: ', x1.varValue)
    print('Toilet paper rolls: ', x2.varValue)
    print('Candles: ', x3.varValue)
    print('Rubber gloves: ', x4.varValue)
    print('Dish soap bottles: ', x5.varValue)
else:
    print('The model is infeasible')
```

## 6: Symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'diapers'), 
        ('x2', 'toilet paper rolls'), 
        ('x3', 'candles'), 
        ('x4', 'rubber gloves'), 
        ('x5', 'dish soap bottles')
    ], 
    'objective_function': '2*x1 + 5*x2 + 8*x3 + 6*x4 + 5*x5', 
    'constraints': [
        '3*x1 <= 216',
        '2*x2 <= 216',
        '11*x3 <= 216',
        '11*x4 <= 216',
        '12*x5 <= 216',
        '2*x2 + 11*x3 >= 26',
        '3*x1 + 11*x4 >= 43',
        '11*x3 + 11*x4 >= 16',
        '11*x3 + 12*x5 >= 39',
        '2*x2 + 12*x5 >= 31',
        '2*x2 + 11*x4 >= 40',
        '3*x1 + 2*x2 >= 20',
        '2*x2 + 11*x3 <= 121',
        '3*x1 + 2*x2 <= 100',
        '3*x1 + 11*x3 <= 62',
        '3*x1 + 2*x2 + 11*x4 <= 88',
        '3*x1 + 11*x4 + 12*x5 <= 179',
        '3*x1 + 2*x2 + 12*x5 <= 69',
        '3*x1 + 2*x2 + 11*x3 <= 152',
        '2*x2 + 11*x3 + 12*x5 <= 150',
        '3*x1 + 2*x2 + 11*x3 + 11*x4 + 12*x5 <= 150'
    ]
}
```