## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'cherry trees' and 'sunflowers', which we can denote as $x_1$ and $x_2$ respectively. The objective function to minimize is $6.23x_1 + 4.36x_2$. The constraints are:
- $19x_1 + 9x_2 \geq 88$ (total water need)
- $18x_1 + 30x_2 \geq 59 \times 144$ (total planting space, converting sq. ft to sq. in)
- $8x_1 + 32x_2 \geq 24$ (total beauty rating)
- $5x_1 + 27x_2 \geq 98$ (total growth speed)
- $25x_1 + x_2 \geq 32$ (total yield)
- $7x_1 - 3x_2 \geq 0$
- $19x_1 + 9x_2 \leq 221$ (total water need upper limit)
- $18x_1 + 30x_2 \leq 62 \times 144$ (total planting space upper limit)
- $8x_1 + 32x_2 \leq 39$ (total beauty rating upper limit)
- $5x_1 + 27x_2 \leq 289$ (total growth speed upper limit)
- $25x_1 + x_2 \leq 161$ (total yield upper limit)
- $x_1, x_2 \geq 0$ and are integers.

## 2: Convert the problem into a Gurobi-compatible format
We need to adjust the units for consistency, particularly for planting space which is given in square feet but needs to be in square inches for consistency with other constraints.

## 3: Write down the symbolic representation
The symbolic representation can be summarized as:
```json
{
'sym_variables': [('x1', 'cherry trees'), ('x2', 'sunflowers')],
'objective_function': '6.23*x1 + 4.36*x2',
'constraints': [
    '19*x1 + 9*x2 >= 88',
    '18*x1 + 30*x2 >= 59*144',
    '8*x1 + 32*x2 >= 24',
    '5*x1 + 27*x2 >= 98',
    '25*x1 + x2 >= 32',
    '7*x1 - 3*x2 >= 0',
    '19*x1 + 9*x2 <= 221',
    '18*x1 + 30*x2 <= 62*144',
    '8*x1 + 32*x2 <= 39',
    '5*x1 + 27*x2 <= 289',
    '25*x1 + x2 <= 161'
]
}
```

## 4: Implement the problem in Gurobi
Now, let's implement this problem using Gurobi in Python.

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="cherry_trees", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="sunflowers", vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(6.23*x1 + 4.36*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(19*x1 + 9*x2 >= 88, name="water_need")
    model.addConstr(18*x1 + 30*x2 >= 59*144, name="planting_space")
    model.addConstr(8*x1 + 32*x2 >= 24, name="beauty_rating")
    model.addConstr(5*x1 + 27*x2 >= 98, name="growth_speed")
    model.addConstr(25*x1 + x2 >= 32, name="yield")
    model.addConstr(7*x1 - 3*x2 >= 0, name="cherry_sunflower_ratio")
    model.addConstr(19*x1 + 9*x2 <= 221, name="max_water_need")
    model.addConstr(18*x1 + 30*x2 <= 62*144, name="max_planting_space")
    model.addConstr(8*x1 + 32*x2 <= 39, name="max_beauty_rating")
    model.addConstr(5*x1 + 27*x2 <= 289, name="max_growth_speed")
    model.addConstr(25*x1 + x2 <= 161, name="max_yield")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Cherry trees: {x1.varValue}")
        print(f"Sunflowers: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```