## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's define the variables as follows:

- $x_1$ = coffees
- $x_2$ = hot chocolates

The objective is to maximize profit. The profit on a coffee is $0.22, and the profit on a hot chocolate is $0.14. Therefore, the objective function can be represented as:

Maximize: $0.22x_1 + 0.14x_2$

The constraints based on the problem description are:

1. $x_1 \geq 40$ (at least 40 coffees)
2. $x_2 \geq 20$ (at least 20 hot chocolates)
3. $x_1 \leq 60$ (no more than 60 coffees)
4. $x_2 \leq 35$ (no more than 35 hot chocolates)
5. $x_1 + x_2 \leq 75$ (no more than 75 items total)

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'coffees'), ('x2', 'hot chocolates')],
    'objective_function': '0.22*x1 + 0.14*x2',
    'constraints': [
        'x1 >= 40',
        'x2 >= 20',
        'x1 <= 60',
        'x2 <= 35',
        'x1 + x2 <= 75'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
m = gp.Model("coffee_hot_chocolate")

# Define variables
x1 = m.addVar(name="coffees", lb=0, ub=gp.GRB.INFINITY, vtype=gp.GRB.INTEGER)
x2 = m.addVar(name="hot_chocolates", lb=0, ub=gp.GRB.INFINITY, vtype=gp.GRB.INTEGER)

# Objective function: Maximize profit
m.setObjective(0.22*x1 + 0.14*x2, gp.GRB.MAXIMIZE)

# Constraints
m.addConstr(x1 >= 40, name="min_coffees")
m.addConstr(x2 >= 20, name="min_hot_chocolates")
m.addConstr(x1 <= 60, name="max_coffees")
m.addConstr(x2 <= 35, name="max_hot_chocolates")
m.addConstr(x1 + x2 <= 75, name="total_items")

# Update model
m.update()

# Solve model
m.optimize()

# Print solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Coffees: {x1.varValue}")
    print(f"Hot Chocolates: {x2.varValue}")
    print(f"Maximum Profit: {0.22*x1.varValue + 0.14*x2.varValue}")
else:
    print("No optimal solution found.")
```