## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- 'x1' or 'hamburgers'
- 'x2' or 'ham sandwiches'
- 'x3' or 'strawberries'
- 'x4' or 'hot dogs'
- 'x5' or 'green beans'
- 'x6' or 'oranges'

## Step 2: Convert the problem description into a symbolic representation
The symbolic representation of the variables is:
```json
{
'sym_variables': [
    ('x1', 'hamburgers'), 
    ('x2', 'ham sandwiches'), 
    ('x3', 'strawberries'), 
    ('x4', 'hot dogs'), 
    ('x5', 'green beans'), 
    ('x6', 'oranges')
]
}
```

## Step 3: Define the objective function in symbolic notation
The objective function to maximize is: 
\[ 7.24x_1 + 8.67x_2 + 5.02x_3 + 8.69x_4 + 4.33x_5 + 3.76x_6 \]

## Step 4: List the constraints in symbolic notation
The constraints are:
- $x_1 \geq 0$ ( implicit, as there's no lower bound given )
- $x_2 \geq 0$ ( implicit, as there's no lower bound given )
- $x_3 \geq 0$ ( implicit, as there's no lower bound given )
- $x_4 \geq 0$ ( implicit, as there's no lower bound given )
- $x_5 \geq 0$ ( implicit, as there's no lower bound given )
- $x_6 \geq 0$ ( implicit, as there's no lower bound given )
- $1x_1 + 2x_2 + 7x_3 + 6x_4 + 1x_5 + 2x_6 \leq 139$
- $2x_2 + 7x_3 \geq 17$
- $2x_2 + 6x_4 \geq 18$
- $6x_4 + 2x_6 \geq 14$
- $7x_3 + 6x_4 \geq 7$
- $1x_1 + 2x_6 \geq 18$
- $6x_4 + 1x_5 \geq 17$
- $1x_1 + 7x_3 \geq 13$
- $1x_1 + 2x_2 \geq 23$
- $6x_4 + 1x_5 \leq 138$
- $2x_2 + 1x_5 \leq 57$
- $1x_1 + 2x_2 \leq 50$
- $7x_3 + 6x_4 \leq 122$
- $6x_4 + 2x_6 \leq 138$
- $1x_1 + 2x_6 \leq 129$
- $7x_3 + 2x_6 \leq 121$
- $7x_3 + 1x_5 \leq 122$
- $2x_2 + 2x_6 \leq 48$
- $1x_1 + 2x_2 + 7x_3 + 6x_4 + 1x_5 + 2x_6 \leq 48$

## 5: Write down the complete symbolic problem representation
```json
{
'sym_variables': [
    ('x1', 'hamburgers'), 
    ('x2', 'ham sandwiches'), 
    ('x3', 'strawberries'), 
    ('x4', 'hot dogs'), 
    ('x5', 'green beans'), 
    ('x6', 'oranges')
],
'objective_function': '7.24x1 + 8.67x2 + 5.02x3 + 8.69x4 + 4.33x5 + 3.76x6',
'constraints': [
    '1x1 + 2x2 + 7x3 + 6x4 + 1x5 + 2x6 <= 139',
    '2x2 + 7x3 >= 17',
    '2x2 + 6x4 >= 18',
    '6x4 + 2x6 >= 14',
    '7x3 + 6x4 >= 7',
    'x1 + 2x6 >= 18',
    '6x4 + x5 >= 17',
    'x1 + 7x3 >= 13',
    'x1 + 2x2 >= 23',
    '6x4 + x5 <= 138',
    '2x2 + x5 <= 57',
    'x1 + 2x2 <= 50',
    '7x3 + 6x4 <= 122',
    '6x4 + 2x6 <= 138',
    'x1 + 2x6 <= 129',
    '7x3 + 2x6 <= 121',
    '7x3 + x5 <= 122',
    '2x2 + 2x6 <= 48',
    'x1 + 2x2 + 7x3 + 6x4 + x5 + 2x6 <= 48'
]
}
```

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

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

# Define the variables
x1 = m.addVar(name='hamburgers', lb=0, ub=None)
x2 = m.addVar(name='ham sandwiches', lb=0, ub=None)
x3 = m.addVar(name='strawberries', lb=0, ub=None)
x4 = m.addVar(name='hot dogs', lb=0, ub=None)
x5 = m.addVar(name='green beans', lb=0, ub=None)
x6 = m.addVar(name='oranges', lb=0, ub=None)

# Objective function
m.setObjective(7.24*x1 + 8.67*x2 + 5.02*x3 + 8.69*x4 + 4.33*x5 + 3.76*x6, gurobi.GRB.MAXIMIZE)

# Constraints
m.addConstr(1*x1 + 2*x2 + 7*x3 + 6*x4 + 1*x5 + 2*x6 <= 139)
m.addConstr(2*x2 + 7*x3 >= 17)
m.addConstr(2*x2 + 6*x4 >= 18)
m.addConstr(6*x4 + 2*x6 >= 14)
m.addConstr(7*x3 + 6*x4 >= 7)
m.addConstr(x1 + 2*x6 >= 18)
m.addConstr(6*x4 + x5 >= 17)
m.addConstr(x1 + 7*x3 >= 13)
m.addConstr(x1 + 2*x2 >= 23)
m.addConstr(6*x4 + x5 <= 138)
m.addConstr(2*x2 + x5 <= 57)
m.addConstr(x1 + 2*x2 <= 50)
m.addConstr(7*x3 + 6*x4 <= 122)
m.addConstr(6*x4 + 2*x6 <= 138)
m.addConstr(x1 + 2*x6 <= 129)
m.addConstr(7*x3 + 2*x6 <= 121)
m.addConstr(7*x3 + x5 <= 122)
m.addConstr(2*x2 + 2*x6 <= 48)
m.addConstr(x1 + 2*x2 + 7*x3 + 6*x4 + x5 + 2*x6 <= 48)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', m.objVal)
    print('hamburgers: ', x1.varValue)
    print('ham sandwiches: ', x2.varValue)
    print('strawberries: ', x3.varValue)
    print('hot dogs: ', x4.varValue)
    print('green beans: ', x5.varValue)
    print('oranges: ', x6.varValue)
else:
    print('No solution found')
```