## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'aloe vera' and 'begonias', which we can denote as $x_1$ and $x_2$ respectively. The objective function to maximize is $7.44x_1 + 7.67x_2$. The constraints are as follows:
- $22x_1 + 5x_2 \geq 14$
- $18x_1 + 13x_2 \geq 59$
- $6x_1 + 8x_2 \geq 59$
- $3x_1 + 17x_2 \geq 42$
- $22x_1 + 14x_2 \geq 31$
- $8x_1 - 5x_2 \geq 0$
- $22x_1 + 5x_2 \leq 40$
- $18x_1 + 13x_2 \leq 108$
- $6x_1 + 8x_2 \leq 83$
- $3x_1 + 17x_2 \leq 72$
- $22x_1 + 14x_2 \leq 76$
- $x_1, x_2 \geq 0$ and are integers.

## Step 2: Convert the problem into a Gurobi-compatible format
We need to define the variables, the objective function, and the constraints in a format that Gurobi can understand.

## 3: Write the Gurobi code
```python
import gurobi as gp

# Define the model
model = gp.Model()

# Define the variables
x1 = model.addVar(name="aloe_vera", vtype=gp.GRB.INTEGER)
x2 = model.addVar(name="begonias", vtype=gp.GRB.INTEGER)

# Define the objective function
model.setObjective(7.44 * x1 + 7.67 * x2, gp.GRB.MAXIMIZE)

# Define the constraints
model.addConstr(22 * x1 + 5 * x2 >= 14, name="cost_min")
model.addConstr(18 * x1 + 13 * x2 >= 59, name="water_min")
model.addConstr(6 * x1 + 8 * x2 >= 59, name="growth_min")
model.addConstr(3 * x1 + 17 * x2 >= 42, name="space_min")
model.addConstr(22 * x1 + 14 * x2 >= 31, name="yield_min")
model.addConstr(8 * x1 - 5 * x2 >= 0, name="ratio_constraint")
model.addConstr(22 * x1 + 5 * x2 <= 40, name="cost_max")
model.addConstr(18 * x1 + 13 * x2 <= 108, name="water_max")
model.addConstr(6 * x1 + 8 * x2 <= 83, name="growth_max")
model.addConstr(3 * x1 + 17 * x2 <= 72, name="space_max")
model.addConstr(22 * x1 + 14 * x2 <= 76, name="yield_max")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Aloe Vera: {x1.varValue}")
    print(f"Begonias: {x2.varValue}")
    print(f"Objective: {model.objVal}")
else:
    print("No optimal solution found.")
```

## 4: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'aloe vera'), ('x2', 'begonias')],
    'objective_function': '7.44*x1 + 7.67*x2',
    'constraints': [
        '22*x1 + 5*x2 >= 14',
        '18*x1 + 13*x2 >= 59',
        '6*x1 + 8*x2 >= 59',
        '3*x1 + 17*x2 >= 42',
        '22*x1 + 14*x2 >= 31',
        '8*x1 - 5*x2 >= 0',
        '22*x1 + 5*x2 <= 40',
        '18*x1 + 13*x2 <= 108',
        '6*x1 + 8*x2 <= 83',
        '3*x1 + 17*x2 <= 72',
        '22*x1 + 14*x2 <= 76'
    ]
}
```