## Step 1: Define the symbolic representation of the problem
The problem involves variables 'sunflowers', 'cherry trees', and 'basil plants', which we can denote as $x_0$, $x_1$, and $x_2$ respectively. The objective function to minimize is $1.28x_0 + 1.29x_1 + 3.61x_2$. The constraints are:
- Resilience index of sunflowers: $3.98x_0$
- Yield of sunflowers: $1.58x_0$
- Resilience index of cherry trees: $4.75x_1$
- Yield of cherry trees: $11.98x_1$
- Resilience index of basil plants: $8.04x_2$
- Yield of basil plants: $11.92x_2$
- Total combined resilience index from sunflowers and basil plants: $3.98x_0 + 8.04x_2 \geq 23$
- Total combined resilience index from all: $3.98x_0 + 4.75x_1 + 8.04x_2 \geq 23$
- Total yield from sunflowers and cherry trees: $1.58x_0 + 11.98x_1 \geq 47$
- Total yield from cherry trees and basil plants: $11.98x_1 + 11.92x_2 \geq 56$
- Total yield from all: $1.58x_0 + 11.98x_1 + 11.92x_2 \geq 57$
- $-5x_1 + 2x_2 \geq 0$
- $4x_0 - 3x_2 \geq 0$
- Total yield from sunflowers and cherry trees: $1.58x_0 + 11.98x_1 \leq 88$
- Total yield from sunflowers and basil plants: $1.58x_0 + 11.92x_2 \leq 76$
- $x_0, x_2$ are non-fractional, $x_1$ is an integer.

## Step 2: Convert the problem into a symbolic representation
The symbolic variables are:
- $x_0$ for 'sunflowers'
- $x_1$ for 'cherry trees'
- $x_2$ for 'basil plants'

The objective function is: $1.28x_0 + 1.29x_1 + 3.61x_2$

The constraints are:
1. $3.98x_0 \geq 3.98$ (redundant, as it's a single variable constraint)
2. $1.58x_0 \geq 1.58$ (redundant)
3. $4.75x_1 \geq 4.75$ (redundant)
4. $11.98x_1 \geq 11.98$ (redundant)
5. $8.04x_2 \geq 8.04$ (redundant)
6. $11.92x_2 \geq 11.92$ (redundant)
7. $3.98x_0 + 8.04x_2 \geq 23$
8. $3.98x_0 + 4.75x_1 + 8.04x_2 \geq 23$
9. $1.58x_0 + 11.98x_1 \geq 47$
10. $11.98x_1 + 11.92x_2 \geq 56$
11. $1.58x_0 + 11.98x_1 + 11.92x_2 \geq 57$
12. $-5x_1 + 2x_2 \geq 0$
13. $4x_0 - 3x_2 \geq 0$
14. $1.58x_0 + 11.98x_1 \leq 88$
15. $1.58x_0 + 11.92x_2 \leq 76$

## 3: Formulate the problem in Gurobi
We will use Gurobi to solve this linear programming problem.

```python
import gurobi as gp

# Define the model
m = gp.Model("optimization_problem")

# Define the variables
x0 = m.addVar(name="sunflowers", vtype=gp.GRB.CONTINUOUS) # Non-fractional
x1 = m.addVar(name="cherry_trees", vtype=gp.GRB.INTEGER) # Integer
x2 = m.addVar(name="basil_plants", vtype=gp.GRB.CONTINUOUS) # Non-fractional

# Define the objective function
m.setObjective(1.28*x0 + 1.29*x1 + 3.61*x2, gp.GRB.MINIMIZE)

# Define the constraints
m.addConstr(3.98*x0 + 8.04*x2 >= 23)
m.addConstr(3.98*x0 + 4.75*x1 + 8.04*x2 >= 23)
m.addConstr(1.58*x0 + 11.98*x1 >= 47)
m.addConstr(11.98*x1 + 11.92*x2 >= 56)
m.addConstr(1.58*x0 + 11.98*x1 + 11.92*x2 >= 57)
m.addConstr(-5*x1 + 2*x2 >= 0)
m.addConstr(4*x0 - 3*x2 >= 0)
m.addConstr(1.58*x0 + 11.98*x1 <= 88)
m.addConstr(1.58*x0 + 11.92*x2 <= 76)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Sunflowers: ", x0.varValue)
    print("Cherry Trees: ", x1.varValue)
    print("Basil Plants: ", x2.varValue)
else:
    print("The model is infeasible")
```

## 4: Provide the symbolic representation in JSON format
```json
{
    "sym_variables": [
        ["x0", "sunflowers"],
        ["x1", "cherry trees"],
        ["x2", "basil plants"]
    ],
    "objective_function": "1.28x0 + 1.29x1 + 3.61x2",
    "constraints": [
        "3.98x0 + 8.04x2 >= 23",
        "3.98x0 + 4.75x1 + 8.04x2 >= 23",
        "1.58x0 + 11.98x1 >= 47",
        "11.98x1 + 11.92x2 >= 56",
        "1.58x0 + 11.98x1 + 11.92x2 >= 57",
        "-5x1 + 2x2 >= 0",
        "4x0 - 3x2 >= 0",
        "1.58x0 + 11.98x1 <= 88",
        "1.58x0 + 11.92x2 <= 76"
    ]
}
```