## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'aloe vera' and 'cherry trees', which we can denote as $x_0$ and $x_1$ respectively. The objective function to maximize is $6x_0 + 7x_1$. The constraints are:
- $x_0 \geq 0$ and $x_1 \geq 0$ (non-negativity, but since they are counts, they are implicitly integers)
- $1x_0 + 15x_1 \geq 39$ (total spend at least $39)
- $7x_0 + 16x_1 \geq 14$ (total water need at least 14 inches)
- $9x_0 + 12x_1 \geq 38$ (total yield at least 38)
- $14x_0 + 18x_1 \geq 26$ (total resilience index at least 26)
- $-4x_0 + 7x_1 \geq 0$ (specific linear constraint)
- $1x_0 + 15x_1 \leq 72$ (total spend no more than $72)
- $7x_0 + 16x_1 \leq 55$ (total water need no more than 55 inches)
- $9x_0 + 12x_1 \leq 99$ (total yield no more than 99)
- $14x_0 + 18x_1 \leq 57$ (total resilience index no more than 57)
- $x_0, x_1$ 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 way that Gurobi can understand.

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

# Define the model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name="aloe_vera", vtype=gurobi.GRB.INTEGER)
x1 = model.addVar(name="cherry_trees", vtype=gurobi.GRB.INTEGER)

# Define the objective function
model.setObjective(6 * x0 + 7 * x1, gurobi.GRB.MAXIMIZE)

# Define the constraints
model.addConstr(x0 + 15 * x1 >= 39, name="min_spend")
model.addConstr(7 * x0 + 16 * x1 >= 14, name="min_water_need")
model.addConstr(9 * x0 + 12 * x1 >= 38, name="min_yield")
model.addConstr(14 * x0 + 18 * x1 >= 26, name="min_resilience")
model.addConstr(-4 * x0 + 7 * x1 >= 0, name="linear_constraint")
model.addConstr(x0 + 15 * x1 <= 72, name="max_spend")
model.addConstr(7 * x0 + 16 * x1 <= 55, name="max_water_need")
model.addConstr(9 * x0 + 12 * x1 <= 99, name="max_yield")
model.addConstr(14 * x0 + 18 * x1 <= 57, name="max_resilience")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Aloe Vera: {x0.varValue}, Cherry Trees: {x1.varValue}")
else:
    print("No optimal solution found.")
```

## 4: Symbolic Representation
The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x0', 'aloe vera'), ('x1', 'cherry trees')],
    'objective_function': '6*x0 + 7*x1',
    'constraints': [
        'x0 + 15*x1 >= 39',
        '7*x0 + 16*x1 >= 14',
        '9*x0 + 12*x1 >= 38',
        '14*x0 + 18*x1 >= 26',
        '-4*x0 + 7*x1 >= 0',
        'x0 + 15*x1 <= 72',
        '7*x0 + 16*x1 <= 55',
        '9*x0 + 12*x1 <= 99',
        '14*x0 + 18*x1 <= 57'
    ]
}
```

```python
import gurobi

# Define the model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name="aloe_vera", vtype=gurobi.GRB.INTEGER, lb=0)
x1 = model.addVar(name="cherry_trees", vtype=gurobi.GRB.INTEGER, lb=0)

# Define the objective function
model.setObjective(6 * x0 + 7 * x1, gurobi.GRB.MAXIMIZE)

# Define the constraints
model.addConstr(x0 + 15 * x1 >= 39, name="min_spend")
model.addConstr(7 * x0 + 16 * x1 >= 14, name="min_water_need")
model.addConstr(9 * x0 + 12 * x1 >= 38, name="min_yield")
model.addConstr(14 * x0 + 18 * x1 >= 26, name="min_resilience")
model.addConstr(-4 * x0 + 7 * x1 >= 0, name="linear_constraint")
model.addConstr(x0 + 15 * x1 <= 72, name="max_spend")
model.addConstr(7 * x0 + 16 * x1 <= 55, name="max_water_need")
model.addConstr(9 * x0 + 12 * x1 <= 99, name="max_yield")
model.addConstr(14 * x0 + 18 * x1 <= 57, name="max_resilience")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Aloe Vera: {x0.varValue}, Cherry Trees: {x1.varValue}")
else:
    print("No optimal solution found.")
```